Predicting human developmental toxicity of pharmaceuticals using human stem-like cells and metabolomics

ABSTRACT

The invention provides biomarker profiles of metabolites and methods for screening chemical compounds including pharmaceutical agents, lead and candidate drug compounds and other chemicals using human stem-like cells (hSLCs) or lineage-specific cells produced therefrom. The inventive methods are useful for testing toxicity, particularly developmental toxicity and detecting teratogenic effects of such chemical compounds. Specifically, a more predictive developmental toxicity model, based on an in vitro method that utilizes both hSLCs and metabolomics to discover biomarkers of developmental toxicity is disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 13/069,326, filed Mar. 22, 2011, which claims the benefit of U.S.Ser. No. 61/316,165, filed Mar. 22, 2010 and U.S. Ser. No. 61/394,426,filed Oct. 19, 2010, the entire contents of which are incorporatedherein by reference.

BACKGROUND OF THE INVENTION

Field of the Invention

This invention provides methods for toxicological screening ofpharmaceuticals and other chemical compounds. The invention specificallyprovides assays that involve multipotent human stem-like cells (hSLCs),as well as methods for using these cells to detect developmentaltoxicity or teratogenic effects of pharmaceutical compounds and otherchemicals. More particularly, the invention provides an in vitro meansfor analyzing toxicity of compounds predictive of their toxicity duringhuman development. Candidate predictive biomarkers for toxic orteratogenic effects are also identified and provided herein.

Background Art

Birth defects are a leading cause of infant morbidity and pediatricdisorders in the United States, affecting 1 in every 33 infants born(Brent & Beckman, 1990, Bull NY Acad Med 66: 123-63; Rosano et al.,2000, J. Epidemiology Community Health 54:660-66), or approximately125,000 newborns per year. It is understood that developmental toxicitycan cause birth defects, and can generate embryonic lethality,intrauterine growth restriction (IUGR), dysmorphogenesis (such asskeletal malformations), and functional toxicity, which can lead tocognitive disorders such as autism. There is an increasing concern aboutthe role that chemical exposure can play in the onset of thesedisorders. Indeed, it is estimated that 5% to 10% of all birth defectsare caused by in utero exposure to known teratogenic agents which inducedevelopmental abnormalities in the fetus (Beckman & Brent, 1984, AnnuRev Pharmacol 24: 483-500).

Concern exists that chemical exposure may be playing a significant andpreventable role in producing birth defects (Claudio et al., 2001,Environm Health Perspect 109: A254-A261). This concern has beendifficult to evaluate, however, since the art has lacked a robust andefficient model for testing developmental toxicity for the more than80,000 chemicals in the market, plus the new 2,000 compounds introducedannually (General Accounting Office (GAO), 1994, Toxic SubstancesControl Act: Preliminary Observations on Legislative Changes to MakeTSCA More Effective, Testimony, Jul. 13, 1994, GAO/T-RCED-94-263). Fewerthan 5% of these compounds have been tested for reproductive outcomesand even fewer for developmental toxicity (Environmental ProtectiveAgency (EPA), 1998, Chemical Hazard Data Availability Study, Office ofPollution Prevention and Toxins). Although some attempts have been madeto use animal model systems to assess toxicity (Piersma, 2004,Toxicology Letters 149:147-53), inherent differences in the sensitivityof humans in utero have limited the predictive usefulness of suchmodels. Development of a human-based cell model system would have anenormous impact in drug development and risk assessment of chemicals.

Toxicity, particularly developmental toxicity, is also a major obstaclein the progression of compounds through the drug development process.Currently, toxicity testing is conducted on animal models as a means topredict adverse effects of compound exposure, particularly ondevelopment and organogenesis in human embryos and fetuses. The mostprevalent models that contribute to FDA approval of investigational newdrugs are whole animal studies in rabbits and rats (Piersma, 2004,Toxicology Letters 149: 147-53). In vivo studies rely on administrationof compounds to pregnant animals at different stages of pregnancy andembryonic/fetal development (first week of gestation, organogenesisstage and full gestation length). However, these in vivo animal modelsare limited by a lack of biological correlation between animal and humanresponses to chemical compounds during development due to differences inbiochemical pathways. Species differences are often manifested in trendssuch as dose sensitivity and pharmacokinetic processing of compounds.According to the reported literature, animal models are approximately60% efficient in predicting human developmental response to compounds(Greaves et al., 2004, Nat Rev Drug Discov 3:226-36). Thus,human-directed predictive in vitro models present an opportunity toreduce the costs of new drug development and enable safer drugs.

In vitro models have been employed in the drug industry for over 20years (Huuskonen, 2005, Toxicology & Applied Pharm 207:S495-S500). Manyof the current in vitro assays involve differentiation models usingprimary cell cultures or immortalized cells lines (Huuskonen, 2005,Toxicology & Applied Pharm 207:S495-S500). Unfortunately, these modelsdiffer significantly from their in vivo counterparts in their ability toaccurately assess development toxicity. In particular, the ECVAMinitiative (European Center for Validation of Alternative Methods) hasused mouse embryonic stem cells as a screening system for predictivedevelopmental toxicology. The embryonic stem cell test (EST) has beenable to predict the teratogenicity of 78% of the drugs tested, and thetest was reported to be able to differentiate strong teratogens frommoderate/weak or non-embryotoxic compounds (Spielmann et al., 1997, Invitro Toxicology 10:119-27). This model is limited in part becausetoxicological endpoints are defined only for compounds that impaircardiac differentiation. This model also fails to account forinterspecies developmental differences between mice and humans, and sodoes not fully address the need in the art for human-specific modelsystems.

Thus there remains a need in the art for a human cell derived in vitromethod for reliably determining developmental toxicity in pharmaceuticalagents and other chemical compounds. There also is a need in the art tobetter understand human development and its perturbation by toxins andother developmental disrupting agents, to assist clinical management ofacquired congenital disorders and the many diseases that share thesebiochemical pathways, such as cancer. Human derived cell based systemsincrease the probability of identifying biomarkers of toxicity that mayboth predict toxicity as well as identify toxicity caused by otherdiseases.

The association of metabolomics and human embryonic stem cells (hESCs)has led to a more effective in vitro human model to predictdevelopmental toxicity. hESCs were first derived from the inner cellmass of blastocysts (Thomson et al. 1998). Given the human embryonicorigin of these cells, an in vitro teratogenicity test using hESCs islikely to produce more accurate human endpoints, while at the same timereducing cost and time and increasing predictability over animalstudies. Metabolomics assesses functional changes in biochemicalpathways by detecting changes to the dynamic set of small molecules thatcomprise the metabolome. The feasibility of metabolomics in biomarkerdiscovery has been demonstrated by multiple studies (Cezar et al. 2007,Tan et al. 1998, Sabatine et al. 2005, Barr et al. 2003, Qu et al.2000).

However, there is an unmet need to develop more accurate methods forhuman developmental toxicity screening and the establishment of a highlypredictive in vitro system for predicting chemical toxicity during earlyhuman development.

The present study discloses the establishment of such a system. Thepresent invention further provides for the assessment of a plurality ofsmall molecules, preferably secreted or excreted from human stem-likecells (hSLCs), and is determined and correlated with health and diseaseor insult state.

The present invention provides a high-throughput developmental toxicityscreen that is more predictive than currently available assays and whichoffers quantitative human endpoints.

BRIEF SUMMARY OF THE INVENTION

The present invention provides reagents and methods for more reliable invitro screening of toxicity and teratogenicity of pharmaceutical andnon-pharmaceutical chemicals on hSLCs.

The invention provides human-specific in vitro methods for reliablydetermining toxicity, particularly developmental toxicity andteratogenicity of pharmaceuticals and other chemical compounds usinghSLCs. As provided herein, hSLCs are useful for assessing toxic effectsof chemical compounds, particularly said toxic and teratogenic effectson human development, thus overcoming the limitations associated withinterspecies animal models.

In particular, the invention demonstrates that metabolite profiles ofhSLCs are altered in response to known disruptors of human development.The invention further shows that the hSLC metabolome is a source ofhuman biomarkers for disease and toxic response.

Thus, the hSLC and metabolomics based model of the present inventionoffers a significant advantage over other studies that use mouse orzebra fish-based models to determine toxicity and teratogenicity ofchemical compounds in that the present invention utilizes an all humansystem and human biomarkers to understand the mechanisms of humandevelopmental toxicity.

In one embodiment, the invention discloses a method of predictingteratogenicity of a test compound, comprising the steps of:

-   -   a) culturing hSLCs:        -   i) in the presence of a first known teratogenic compound;            and        -   ii) in the absence of the first known teratogenic compound;    -   b) detecting a plurality of metabolites having a molecular        weight of less than about 3000 Daltons associated with hSLCs        exposed to the first known teratogenic compound in comparison        with hSLCs not exposed to the first known teratogenic compound        in order to identify a difference in metabolic response of hSLCs        exposed to the first known teratogenic compound in comparison        with hSLCs not exposed to the first known teratogenic compound;    -   c) analyzing the difference in metabolic response in order to        generate a set of mass features associated with exposure of        hSLCs to the first teratogenic compound;    -   d) repeating steps a)-c) multiple times, each time with a        different known teratogenic compound;    -   e) grouping mass features generated from each exposure to a        teratogenic compound to obtain a first reference profile of mass        features;    -   f) comparing a profile of mass features generated upon exposure        of hSLCs to a test compound with the first reference profile to        predict the teratogenicity of the test compound;    -   g) if the test compound is predicted to be a teratogen, adding        the profile of mass features to the first reference profile to        obtain a second reference profile, wherein the predictive        accuracy of the second reference profile is greater than the        predictive accuracy of the first reference profile; and    -   h) repeating steps f) and g) multiple times, each time with a        different test compound to obtain a final reference profile.

In another embodiment, the invention discloses a method for classifyinga test compound as a teratogen, the method comprising the steps of:

-   -   a) culturing hSLCs:        -   i) in the presence of the test compound; and        -   ii) in the absence of the test compound;    -   b) identifying a difference in metabolic response of hSLCs in        the presence of the test compound in comparison with hSLCs        cultured in the absence of the test compound by measuring a        plurality of metabolites having a molecular weight of less than        about 3000 Daltons associated with hSLCs, wherein a difference        in the plurality of metabolites associated with hSLCs cultured        in the presence of the test compound versus hSLCs cultured in        the absence of the test compound indicates a difference in        metabolic response; and    -   c) determining the metabolic response of hSLCs involving a first        metabolite to the metabolic response of hSLCs involving a second        metabolite, wherein        -   i) the first metabolite is a precursor of the second            metabolite; or        -   ii) the first metabolite is an amino acid and the second            metabolite is an inhibitor of the metabolism of the amino            acid,

and wherein a difference in the metabolic response of hSLCs involvingthe first metabolite to the metabolic response of hSLCs involving thesecond metabolite is indicative of the test compound being a teratogen.

In yet another embodiment, the invention discloses a method ofclassifying a test compound as a teratogen or a non-teratogen,comprising the steps of:

-   -   a) culturing hSLCs:        -   i) in the presence of the test compound; and        -   ii) in the absence of the test compound;    -   b) determining the fold change in arginine associated with hSLCs        cultured in the presence of the test compound in comparison with        hSLCs cultured in the absence of the test compound;    -   c) determining the fold change in asymmetric dimethyl arginine        (ADMA) associated with hSLCs cultured in the presence of the        test compound in comparison with hSLCs cultured in the absence        of the test compound;    -   d) determining the ratio of the fold change in arginine to the        fold change in ADMA, wherein:        -   i) a ratio of less than at least about 0.9 or greater than            at least about 1.1 is indicative of the teratogenicity of            the test compound; and        -   ii) a ratio of greater than at least about 0.9 and less than            at least about 1.1 is indicative of the non-teratogenicity            of the test compound.

In a further embodiment, the invention discloses a method for validatinga test compound as a teratogen, comprising:

-   -   a) providing, in solid form, a set of metabolites having a        molecular weight of less than about 3000 Daltons, wherein the        metabolites are differentially metabolized by hSLCs cultured in        the presence of one or more known teratogenic compounds in        comparison with hSLCs cultured in the absence of a teratogenic        compound;    -   b) resuspending the set of metabolites in a predetermined volume        of a physiologically suitable buffer, wherein the final        concentration of each metabolite in the buffer is identical to        the concentration of that metabolite associated with hSLCs        cultured in the presence of one or more known teratogenic        compounds;    -   c) generating a reference profile of the metabolites; and    -   d) comparing a profile of mass features generated upon exposure        of hSLCs to the test compound with the reference profile of        metabolites in order to validate the teratogenicity of the test        compound.

In yet another embodiment, the invention discloses a method ofidentifying a metabolic effect of a teratogenic compound, comprising:

-   -   a) culturing hSLCs:        -   i) in the presence of the teratogenic compound; and        -   ii) in the absence of the teratogenic compound;    -   b) detecting a plurality of metabolites having a molecular        weight of less than about 3000 Daltons associated with hSLCs        exposed to the teratogenic compound in comparison with hSLCs not        exposed to the teratogenic compound in order to identify a        difference in metabolic response of hSLCs exposed to the        teratogenic compound in comparison with hSLCs not exposed to the        teratogenic compound;    -   c) mapping the plurality of metabolites to one or more metabolic        networks; and    -   d) identifying a metabolic effect of the teratogenic compound        when the plurality of metabolites are identical to metabolites        affected by a known disruption of the one or more metabolic        networks.

Specific preferred embodiments of the present invention will becomeevident from the following more detailed description of certainpreferred embodiments and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

FIG. 1 illustrates the experimental design used in the present study.Three plate replicates with three well replicates were used for controls(cells with undosed media) and experimental cells (dosed cells). Threewell replicates were used for media control (no cells, undosed media)and dosed media controls (no cells, dosed media).

FIG. 2 illustrates cell viability data that has been normalized tocontrol, undosed cells.

FIG. 3 shows multidimensional scaling plot of the of Random Forest model(similarity metric) showing a clear separation of drugs based onteratogenicity. The circled drug treatments mark rifampicin and accutanethat were misclassified as non-teratogens by the random forest model.Gray=Teratogen, Black=Non-Teratogen, point=first letter of drug.

FIG. 4 illustrates a receiver operating characteristic (ROC) curve basedon the 18-feature refined random forest model.

FIG. 5 depicts a specific step of the urea cycle involving metabolism ofL-arginine to L-citrulline. NO is released when the enzyme nitric oxidesynthase (NOS) oxidizes L-arginine to L-citrulline. Dimethylarginineinhibits nitric oxide synthase. Nitric oxide has been shown to induceNeural Tube Defects (NTD) in rat embryos.

FIG. 6 illustrates the metabolic network relationships between themetabolites found in this study.

FIG. 7 illustrates the experimental design in 96-well plates for dosingexperiments used in the present study.

FIG. 8 depicts data preprocessing flow diagram outlier and overview ofthe filters applied during data processing.

FIG. 9 depicts an overview of the statistical analysis process.

FIG. 10 depicts a viability assay. Cytotoxicity ratios normalized to theuntreated cells (controls) present for each 96-well plate. Bars markedwith an asterisk indicate a statistically significant decrease (pvalue<0.05) in viability:cytotoxicity ratios by a Welch T-test. Chemicalcompound treatments ST003G-74-A, ST003G-80G, and ST0003G-81H exhibitunexpected viability results where low dose appears more toxic than 10×.Drug treatments ST003G-84K, and ST003G-85L do not exhibit a decrease inviability associated with an increase in dosage.

FIG. 11 depicts the nicotinate and nicotinamide metabolic network. Inthis figure and FIGS. 12-28 that follow, all of the features across all12 treatment compounds that were putatively annotated with KEGG ID's andidentified as significant in the pathways enrichment analysis werereviewed for fold changes and marked with black circles in the pathwaydiagrams. Isobaric enzymes are marked with grey circles. Enzymes areidentified with EC codes and identified human enzyme activity ishighlighted in grey.

FIG. 12 depicts the pantothenate and coenzyme A biosynthesis pathway,wherein the respective pathways are modified as disclosed herein.

FIG. 13 depicts the glutathione metabolic network, wherein the pathwayis modified according to the present disclosure.

FIG. 14 depicts the arginine and proline metabolic network, wherein thepathway is modified according to the present disclosure.

FIG. 15 depicts the cysteine and methionine metabolic network, whereinthe pathway is modified according to the present disclosure.

FIG. 16 depicts the pentose phosphate pathway, wherein the pathway ismodified according to the present disclosure.

FIG. 17 depicts the pentose and glucoronate interconversions pathway,wherein the pathway is modified according to the present disclosure.

FIG. 18 depicts the galactose metabolic network, wherein the pathway ismodified according to the present disclosure.

FIG. 19 depicts the ascorbate and aldarate metabolic network, whereinthe pathway is modified according to the present disclosure.

FIG. 20 depicts the purine and pyrimidine metabolic networks, whereinthe pathway is modified according to the present disclosure.

FIG. 21 depicts the valine, leucine, and isoleucine degradation pathway,wherein the pathway is modified according to the present disclosure.

FIG. 22 depicts the lysine biosynthesis and lysine degradation pathways,wherein the pathway is modified according to the present disclosure.

FIG. 23 depicts the amino sugar and nucleotide sugar metabolic network,wherein the pathway is modified according to the present disclosure.

FIG. 24 depicts the pyruvate metabolic network, wherein the pathway ismodified according to the present disclosure.

FIG. 25 depicts the propanoate metabolism and thiamine metabolicnetworks, wherein the respective pathways are modified as disclosedherein.

FIG. 26 depicts the vitamin B6 metabolic network, wherein the pathway ismodified according to the present disclosure.

FIG. 27 depicts the nicotinate and nicotinamide metabolic networks,wherein the respective pathways are modified as disclosed herein.

FIG. 28 depicts the folate biosynthesis pathway, wherein the pathway ismodified according to the present disclosure.

FIG. 29 illustrates cell viability data following doxylamine dosing ofhES cells.

The present invention will now be described with reference to theaccompanying drawings. It is understood that the drawings of the presentapplication are not necessarily drawn to scale and that these figuresand illustrations merely illustrate, but do not limit, the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

The invention provides reagents that are hSLCs, or hESC-derivedlineage-specific cells, such as neural stem cells, neural precursorcells and neural cells produced therefrom, for assessing developmentaltoxicity using the human embryonic stem cell metabolome. hESCs arepluripotent, self-renewing cells isolated directly from preimplantationhuman embryos that recapitulate organogenesis in vitro. Lineage-specificprecursor cells are derived from hESCs and have entered a specificcellular lineage, but yet remain multipotent with regard to cell typewithin that specific lineage. For example, neural precursors havecommitted to neural differentiation but yet remain unrestricted as toits neural cell type. Biochemical pathways of human development anddisease are active in hSLCs, because they recapitulate differentiationinto functional somatic cells. Disruption of these pathways duringdevelopment contributes to disorders such as neural tube defects (NTDs)and cognitive impairment. Environmental agents, namely chemicals ordrugs, participate in the ontogenesis of certain acquired congenitaldisorders.

This specification discloses one or more embodiments that incorporatethe features of this invention. The disclosed embodiment(s) merelyexemplify the invention. The scope of the invention is not limited tothe disclosed embodiment(s). The invention is defined by the claimsappended hereto.

In the following description, for purposes of explanation, specificnumbers, parameters and reagents are set forth in order to provide athorough understanding of the invention. It is understood, however, thatthe invention can be practiced without these specific details. In someinstances, well-known features can be omitted or simplified so as not toobscure the present invention.

The embodiment(s) described, and references in the specification to “oneembodiment”, “an embodiment of the invention”, “an embodiment”, “anexample embodiment”, etc., indicate that the embodiment(s) described mayinclude a particular feature, structure, or characteristic, but everyembodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is understood that it is within the knowledge of oneskilled in the art to effect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

The description of “a” or “an” item herein may refer to a single item ormultiple items. For example, the description of a feature, a protein, abiological fluid, or a classifier may refer to a single feature, aprotein, a biological fluid, or a classifier. Alternatively, thedescription of a feature, a protein, a biological fluid, or a classifiermay refer to multiple features, proteins, biological fluids, orclassifiers. Thus, as used herein, “a” or “an” may be singular orplural. Similarly, references to and descriptions of plural items mayrefer to single items.

It is understood that wherever embodiments are described herein with thelanguage “comprising,” otherwise analogous embodiments described interms of “consisting of” and/or “consisting essentially of” are alsoprovided.

The specification describes methods and kits for predicting and assayingteratogenicity of test compounds as well as methods for assaying testcompounds for neural development disruption by detecting a specific setof purified cellular metabolites having a molecular weight of less thanabout 3000 Daltons that are differentially hSLCs cultured in thepresence of known teratogenic compounds in comparison with hSLCscultured in the absence the known teratogenic compounds. In certainembodiments, the metabolites have a molecular weight from about 50 toabout 3000 Daltons. Specific exemplary embodiments for detecting markerproteins in the serum are provided herein. However, based on theteaching and guidance presented herein, it is understood that it iswithin the knowledge of one skilled in the art to readily adapt themethods described herein to.

Definitions

The metabolome, defined as the total dynamic set of cellular metabolitescreated through cellular metabolism, is a product of health ordisease/insult states. Metabolites include but are not limited tosugars, organic acids, amino acids and fatty acids, particularly thosespecies secreted, excreted, consumed, or identified by the cells, orthose metabolites that are fluxed through the cells, that participate infunctional mechanisms of cellular response to pathological or chemicalinsult. These metabolites serve as biomarkers of disease or toxicresponse and can be detected in biological fluids (Soga et al., 2006, JBiol Chem 281:16768-78; Zhao et al., 2006, Birth Defects Res A Clin MolTeratol 76:230-6), including hSLC culture media. Importantly,metabolomic profiling may confirm functional changes that are oftenpredicted by transcriptomics and proteomics.

Because it was known that hSLCs are highly sensitive to the culturemicroenvironment (Levenstein et al., 2005, Stem Cells 24: 568-574; Li etal., 2005, Biotechnol Bioeng 91:688-698), their application as a sourceof predictive biomarkers in response to chemical compounds, includingtoxins, teratogens and particularly pharmaceutical agents, drug leadcompounds and candidate compounds in drug development, and theirusefulness in establishing in vitro models of disease and developmentwas uncertain, inter alia because those of skill in the art couldanticipate that exposure to an exogenous chemicals could be highlydetrimental to survival of hSLCs and preclude obtaining usefulinformation from them. This concern has turned out not to be justified.

As used herein, the term “human stem-like cells (hSLCs)” is intended toinclude pluripotent, undifferentiated hESCs, as well as human inducedpluripotent (iPS) cells, and human embryoid bodies.

As used herein, the term “human embryonic stem cells (hESCs)” isintended to include undifferentiated stem cells originally derived fromthe inner cell mass of developing blastocysts, and specificallypluripotent, undifferentiated human stem cells andpartially-differentiated cell types thereof (e.g., downstreamprogenitors of differentiating hESC). As provided herein, in vitrocultures of hESCs are pluripotent and not immortalized, and can beinduced to produce lineage-specific cells and differentiated cell typesusing methods well-established in the art. In preferred embodiments,hESCs useful in the practice of the methods of this invention arederived from preimplantation blastocysts as described by Thomson et al.,in co-owned U.S. Pat. No. 6,200,806. Multiple hESC lines are currentlyavailable in US and UK stem cell banks.

As used herein, the term “human embryoid bodies” are aggregates of cellsderived from human embryonic stem cells. Cell aggregation is imposed byhanging drop, plating upon non-tissue culture treated plates or spinnerflasks; either method prevents cells from adhering to a surface to formthe typical colony growth. Upon aggregation, differentiation isinitiated and the cells begin to a limited extent to recapitulateembryonic development. Embryoid bodies are composed of cells from allthree germ layers: endoderm, ectoderm and mesoderm.

As used herein, the term “human induced pluripotent stem cells”,commonly abbreviated as iPS cells are a type of pluripotent stem cellartificially derived from a non-pluripotent cell, typically an adultsomatic cell, by inducing a forced expression of certain genes. iPScells are believed to be identical to natural pluripotent stem cells,such as embryonic stem cells in many respects, such as the expression ofcertain stem cell genes and proteins, chromatin methylation patterns,doubling time, embryoid body formation, teratoma formation, viablechimera formation, and potency and differentiability.

In one embodiment, the cells of the present invention can also includehSLC-derived lineage specific cells. The terms “hSLC-derived lineagespecific cells”, “stem cell progenitor,” “lineage-specific cell,” “hSLCderived cell” and “differentiated cell” as used herein are intended toencompass lineage-specific cells that are differentiated from hSLCs suchthat the cells have committed to a specific lineage of diminishedpluripotency. For example, hSLC-derived lineage specific cells arederived from hSLCs and have entered a specific cellular lineage, but yetremain multipotent with regard to cell type within that specificlineage. The hSLC-derived lineage specific cells can include, forexample, neural stem cells, neural precursor cells, neural cells,cardiac stem cells, cardiac precursor cells, cardiomyocytes, and thelike. In some embodiments, these hSLC-derived lineage-specific cellsremain undifferentiated with regard to final cell type. For example,neuronal stem cells are derived from hSLCs and have differentiatedenough to commit to neuronal lineage. However, the neuronal precursorretains “stemness” in that it retains the potential to develop into anytype of neuronal cell. Additional cell types includeterminally-differentiated cells derived from hESCs or lineage-specificprecursor cells, for example neural cells.

The term “cellular metabolite” and “metabolite” have been used hereininterchangeably. The terms “cellular metabolite” or “metabolite” as usedherein refer to any small molecule secreted, excreted or identified byhSLCs or any small molecule that is fluxed through hSLCs orlineage-specific precursor cells, for example, neural cells. Inpreferred embodiments, cellular metabolites or metabolites include butare not limited to sugars, organic acids, amino acids, fatty acids,hormones, vitamins, oligopeptides (less than about 100 amino acids inlength), as well as ionic fragments thereof. Cells can also be lysed inorder to measure cellular products present within the cell. Inparticular, said metabolites are less than about 3000 Daltons inmolecular weight, and more particularly from about 50 to about 3000Daltons.

The term “metabolic effect” of a teratogenic compound as used hereinrefers to the difference in a plurality of metabolites of one or moremetabolic networks in hSLCs cultured in presence of the teratogeniccompound in comparison with hSLCs cultured in absence of the teratogeniccompound, or hSLCs cultured in presence of a known non-teratogeniccompound, wherein the plurality of metabolites are identical tometabolites affected by a known disruption of the one or more metabolicnetworks. In one embodiment, the metabolites can be differentiallyexpressed. In one aspect, for example, the expression of the metabolitesis increased when exposed to a teratogenic compound and decreased whenexposed to a non-teratogenic compound. In another aspect, for example,the metabolites are secreted when exposed to a teratogenic compound andnot secreted when exposed to a non-teratogenic compound.

The term “metabolic response” as used herein refers to a change causedthrough alterations in enzyme activity (e.g. regulation by allosteric,covalent modification, or protein processing), enzyme abundance,non-enzymatic chemical reactions, cellular transporters, and action ofenzymes in the extracellular space leading to changes in abundance ofone or more metabolites or flux of media components in response to anexperimental treatment. The response can be measured both by changes inabundance of one or more metabolites in the extracellular orintracellular environment.

In one embodiment, one or more of the measured metabolites is ametabolite secreted from the hSLCs.

In one embodiment, one or more of the measured metabolites is ametabolite excreted from the hSLCs.

In one embodiment, one or more of the measured metabolites is ametabolite consumed by the hSLCs.

In one embodiment, one or more of the measured metabolites is ametabolite identified by the hSLCs.

In one embodiment, the difference in metabolic response for thesecreted, excreted, consumed, or identified metabolite associated withhSLCs cultured in the presence of a test compound or a known teratogeniccompound in comparison with hSLCs cultured in the absence of a testcompound or a known teratogenic compound is determined by measuring theflux of the metabolite through the hSLCs.

The term “flux” as used herein refers to the turnover of metabolites bycatabolism and/or anabolism through the metabolic networks and networksof an organism. The metabolic footprint observed by measuring thedifferential utilization of media components following treatments ofcultures is an example of metabolic flux.

The term “identified” as used herein refers to cellular metabolites thatare secreted or consumed by hSLCs. The term also encompasses cellularmetabolites that are fluxed through hSLCs.

hSLCs are cultured according to the methods of the invention usingstandard methods of cell culture well-known in the art, including, forexample those methods disclosed in Ludwig et al. (2006,Feeder-independent culture of human embryonic stem cells, Nat Methods 3:637-46). In preferred embodiments, hSLCs are cultured in the absence ofa feeder cell layer during the practice of the inventive methods;however, hSLCs can be cultured on feeder cell layer prior to thepractice of the methods of this invention.

The terms “administering” or “dosing” as used herein refer to contactingin vitro cultures of hSLCs with a toxic, teratogenic, or test chemicalcompound. In a preferred embodiment the dosage of the compound isadministered in an amount equivalent to levels achieved or achievable invivo, for example, in maternal circulation.

The phrases “identifying metabolites that are differentially produced”or “detecting alterations in the cells or alternations in metabolism” asused herein include but are not limited to comparisons of treated hSLCsto untreated (control) cells (i.e., cells cultured in the presence(treated) or absence (untreated) of a toxic, teratogenic, or testchemical compound. Detection or measurement of variations in cellularmetabolites, excreted or secreted or metabolized in the mediumtherefrom, between treated and untreated cells is included in thisdefinition. In a preferred embodiment, alterations in cells or cellactivity are measured by determining a profile of changes in cellularmetabolites having a molecular weight of less than 3000 Daltons, moreparticularly between 50 and 3000 Daltons, in a treated versus untreatedcell.

The terms “metabolic pathway” or “metabolic network” or “metabolismpathway” as used herein refers to a series of chemical reactionsoccurring within a cell. In each pathway, a principal compound ismodified by one or more chemical or enzymatic reactions. Moreover, ametabolic pathway can be composed of a series of biochemical reactionsconnected by their intermediates. The reactants (or substrates) of onereaction can be the products of a previous reaction, and so on.Metabolic pathways are usually considered in one direction (althoughmost reactions are reversible, conditions in the cell are such that itis thermodynamically more favorable for flux to be in one of thedirections). Enzymes catalyze the reactions of a metabolic pathway, andoften require dietary minerals, vitamins, and other cofactors in orderto function properly. Because of the many compounds that may beinvolved, pathways can be quite elaborate. In addition, many pathwayscan exist within a cell. This collection of pathways is called themetabolic network. Metabolic pathways and networks are important to themaintenance of homeostasis within an organism. In one embodiment, acompound comprises one or more biological molecules of a metabolicpathway or network that are modified by one or more chemical orenzymatic reactions. In another embodiment a compound comprises one ormore products of a metabolic pathway or network that are modified by oneor more chemical or enzymatic reactions. In another aspect a compoundcomprises one or more intermediates of a metabolic pathway or networkthat are modified by one or more chemical or enzymatic reactions. In yetanother embodiment a compound comprises one or more reactants of ametabolic pathway or network that are modified by one or more chemicalor enzymatic reactions. Any person of skill in the art would understandthat a metabolic pathway or metabolic network, as defined herein,includes one or more compounds associated with anabolic and/or catabolicmetabolism of a particular metabolite. For example, glutathione pathwaycomprises products or reactants associated with anabolic and/orcatabolic metabolism of glutathione.

The term “correlating” or “associating” or “pattern matching” as usedherein refers to the positive correlation, or association, or matchingof alterations of patterns in cellular metabolites including but notlimited to sugars, organic acids, amino acids, fatty acids, and lowmolecular weight compounds excreted or secreted from hSLCs, to an invivo toxic response. The screened cellular metabolites can be involvedin a wide range of biochemical pathways in the cells and related to avariety of biological activities including, but not limited toinflammation, anti-inflammatory response, vasodilation, neuroprotection,oxidative stress, antioxidant activity, DNA replication and cell cyclecontrol, methylation, and biosynthesis of, inter alia, nucleotides,carbohydrates, amino acids and lipids, among others. Alterations inspecific subsets of cellular metabolites can correspond to a particularmetabolic or developmental pathway and thus reveal effects of a testcompound on in vivo development.

In one embodiment, cellular metabolites are identified using a physicalseparation method.

The term “physical separation method” as used herein refers to anymethod known to those with skill in the art sufficient to produce aprofile of changes and differences in small molecules produced in hSLCs,contacted with a toxic, teratogenic or test chemical compound accordingto the methods of this invention. In a preferred embodiment, physicalseparation methods permit detection of cellular metabolites includingbut not limited to sugars, organic acids, amino acids, fatty acids,hormones, vitamins, and oligopeptides, as well as ionic fragmentsthereof and low molecular weight compounds (preferably with a molecularweight less than 3000 Daltons, and more particularly between 50 and 3000Daltons). For example, mass spectrometry can be used. In particularembodiments, this analysis is performed by liquidchromatography/electrospray ionization time of flight mass spectrometry(LC/ESI-TOF-MS), however it will be understood that cellular metabolitesas set forth herein can be detected using alternative spectrometrymethods or other methods known in the art for analyzing these types ofcellular compounds in this size range.

The term “biomarker” as used herein refers to metabolites that exhibitsignificant alterations between hSLCs cultured in the presence of a testcompound or a known teratogenic compound in comparison with hSLCscultured in the absence of the test compound or the known teratogeniccompound. In one embodiment, at least one of the metabolites is secretedor excreted from the hSLCs or consumed or identified by hSLCs in greateramounts in the presence of the test compound or known teratogeniccompound than in the absence of the test compound or the knownteratogenic compound. In another embodiment, at least one of thecellular metabolites is secreted or excreted from the hSLCs in loweramounts in the presence of the test compound or known teratogeniccompound than in the absence of the test compound or the knownteratogenic compound.

In preferred embodiments, biomarkers are identified by methods includingLC/ESI-TOF-MS and QTOF-MS. Metabolomic biomarkers are identified bytheir unique molecular mass and consistency with which the marker isdetected in response to a particular toxic, teratogenic or test chemicalcompound; thus the actual identity of the underlying compound thatcorresponds to the biomarker is not required for the practice of thisinvention.

Alternatively, certain biomarkers can be identified by, for example,gene expression analysis, including real-time PCR, RT-PCR, Northernanalysis, and in situ hybridization.

In addition, biomarkers can be identified using Mass Spectrometry suchas MALDI/TOF (time-of-flight), SELDI/TOF, liquid chromatography-massspectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), highperformance liquid chromatography-mass spectrometry (HPLC-MS), capillaryelectrophoresis-mass spectrometry, nuclear magnetic resonancespectrometry, tandem mass spectrometry (e.g., MS/MS, MS/MS/MS, ESI-MS/MSetc.), secondary ion mass spectrometry (SIMS), or ion mobilityspectrometry (e.g. GC-IMS, IMS-MS, LC-IMS, LC-IMS-MS etc.).

Mass spectrometry methods are well known in the art and have been usedto quantify and/or identify biomolecules, such as proteins and othercellular metabolites (see, e.g., Li et al., 2000; Rowley et al., 2000;and Kuster and Mann, 1998).

In certain embodiments, a gas phase ion spectrophotometer is used. Inother embodiments, laser-desorption/ionization mass spectrometry is usedto identify biomarkers. Modern laser desorption/ionization massspectrometry (“LDI-MS”) can be practiced in two main variations: matrixassisted laser desorption/ionization (“MALDI”) mass spectrometry andsurface-enhanced laser desorption/ionization (“SELDI”).

In MALDI, the analyte (e.g. biomarkers) is mixed with a solutioncontaining a matrix, and a drop of the liquid is placed on the surfaceof a substrate. The matrix solution then co-crystallizes with thebiomarkers. The substrate is inserted into the mass spectrometer. Laserenergy is directed to the substrate surface where it desorbs and ionizesthe proteins without significantly fragmenting them. However, MALDI haslimitations as an analytical tool. It does not provide means forfractionating the biological fluid, and the matrix material caninterfere with detection, especially for low molecular weight analytes.

In SELDI, the substrate surface is modified so that it is an activeparticipant in the desorption process. In one variant, the surface isderivatized with adsorbent and/or capture reagents that selectively bindthe biomarker of interest. In another variant, the surface isderivatized with energy absorbing molecules that are not desorbed whenstruck with the laser. In another variant, the surface is derivatizedwith molecules that bind the biomarker of interest and that contain aphotolytic bond that is broken upon application of the laser. In each ofthese methods, the derivatizing agent generally is localized to aspecific location on the substrate surface where the sample is applied.The two methods can be combined by, for example, using a SELDI affinitysurface to capture an analyte (e.g. biomarker) and addingmatrix-containing liquid to the captured analyte to provide the energyabsorbing material.

For additional information regarding mass spectrometers, see, e.g.,Principles of Instrumental Analysis, 3rd edition., Skoog, SaundersCollege Publishing, Philadelphia, 1985; and Kirk-Othmer Encyclopedia ofChemical Technology, 4^(th) ed. Vol. 15 (John Wiley & Sons, New York1995), pp. 1071-1094.

In some embodiments, the data from mass spectrometry is represented as amass chromatogram. A “mass chromatogram” is a representation of massspectrometry data as a chromatogram, where the x-axis represents timeand the y-axis represents signal intensity. In one aspect the masschromatogram is a total ion current (TIC) chromatogram. In anotheraspect, the mass chromatogram is a base peak chromatogram. In otherembodiments, the mass chromatogram is a selected ion monitoring (SIM)chromatogram. In yet another embodiment, the mass chromatogram is aselected reaction monitoring (SRM) chromatogram. In a preferredembodiment, the mass chromatogram is an extracted ion chromatogram(EIC).

In an EIC, a single feature is monitored throughout the entire run. Thetotal intensity or base peak intensity within a mass tolerance windowaround a particular analyte's mass-to-charge ratio is plotted at everypoint in the analysis. The size of the mass tolerance window typicallydepends on the mass accuracy and mass resolution of the instrumentcollecting the data. As used herein, the term “feature” refers to asingle small metabolite, or a fragment of a metabolite. In someembodiments, the term feature may also include noise upon furtherinvestigation.

Detection of the presence of a biomarker will typically involvedetection of signal intensity. This, in turn, can reflect the quantityand character of a biomarker bound to the substrate. For example, incertain embodiments, the signal strength of peak values from spectra ofa first sample and a second sample can be compared (e.g., visually, bycomputer analysis etc.) to determine the relative amounts of particularbiomarkers. Software programs such as the Biomarker Wizard program(Ciphergen Biosystems, Inc., Fremont, Calif.) can be used to aid inanalyzing mass spectra. The mass spectrometers and their techniques arewell known.

A person skilled in the art understands that any of the components of amass spectrometer, e.g., desorption source, mass analyzer, detect, etc.,and varied sample preparations can be combined with other suitablecomponents or preparations described herein, or to those known in theart. For example, in some embodiments a control sample may contain heavyatoms, e.g. ¹³C, thereby permitting the test sample to be mixed with theknown control sample in the same mass spectrometry run. Good stableisotopic labeling is included.

In one embodiment, a laser desorption time-of-flight (TOF) massspectrometer is used. In laser desorption mass spectrometry, a substratewith a bound marker is introduced into an inlet system. The marker isdesorbed and ionized into the gas phase by laser from the ionizationsource. The ions generated are collected by an ion optic assembly, andthen in a time-of-flight mass analyzer, ions are accelerated through ashort high voltage field and let drift into a high vacuum chamber. Atthe far end of the high vacuum chamber, the accelerated ions strike asensitive detector surface at a different time. Since the time-of-flightis a function of the mass of the ions, the elapsed time between ionformation and ion detector impact can be used to identify the presenceor absence of molecules of specific mass to charge ratio.

In one embodiment of the invention, levels of biomarkers are detected byMALDI-TOF mass spectrometry.

Methods of detecting biomarkers also include the use of surface plasmonresonance (SPR). The SPR biosensing technology has been combined withMALDI-TOF mass spectrometry for the desorption and identification ofbiomarkers.

Data for statistical analysis can be extracted from chromatograms(spectra of mass signals) using softwares for statistical methods knownin the art. “Statistics” is the science of making effective use ofnumerical data relating to groups of individuals or experiments. Methodsfor statistical analysis are well-known in the art.

In one embodiment a computer is used for statistical analysis.

In one embodiment, the Agilent MassProfiler or MassProfilerProfessionalsoftware is used for statistical analysis. In another embodiment, theAgilent MassHunter software Qual software is used for statisticalanalysis. In other embodiments, alternative statistical analysis methodscan be used. Such other statistical methods include the Analysis ofVariance (ANOVA) test, Chi-square test, Correlation test, Factoranalysis test, Mann-Whitney U test, Mean square weighted derivation(MSWD), Pearson product-moment correlation coefficient, Regressionanalysis, Spearman's rank correlation coefficient, Student's T test,Welch's T-test, Tukey's test, and Time series analysis.

In different embodiments signals from mass spectrometry can betransformed in different ways to improve the performance of the method.Either individual signals or summaries of the distributions of signals(such as mean, median or variance) can be so transformed. Possibletransformations include taking the logarithm, taking some positive ornegative power, for example the square root or inverse, or taking thearcsin (Myers, Classical and Modern Regression with Applications, 2^(nd)edition, Duxbury Press, 1990).

In different embodiments, statistical classification algorithms are usedto create a classification model in order to predict teratogenicity andnon-teratogenicity of test compounds. Machine learning-based classifiershave been applied in various fields such as machine perception, medicaldiagnosis, bioinformatics, brain-machine interfaces, classifying DNAsequences, and object recognition in computer vision. Learning-basedclassifiers have proven to be highly efficient in solving somebiological problems.

As used herein, “classification” is the process of learning to separatedata points into different classes by finding common features betweencollected data points which are within known classes. In statistics,classification is the problem of identifying the sub-population to whichnew observations belong, where the identify of the sub-population isunknown, on the basis of a training set of data containing observationswhose sub-population is known. Thus the requirement is that newindividual items are placed into groups based on quantitativeinformation on one or more measurements, traits or characteristics, etc)and based on the training set in which previously decided groupings arealready established. Classification problem has many applications. Insome cases, it is employed as a data mining procedure, while in othersmore detailed statistical modeling is undertaken.

As used herein, a “classifier” is a method, algorithm, computer program,or system for performing data classification. Examples of widely usedclassifiers include, but are not limited to, the Neural network(multi-layer perceptron), Support vector machines, k-nearest neighbors,Gaussian mixture model, Gaussian, naive Bayes, Decision tree, and RBFclassifiers.

In some embodiments, classification models to predict teratogenicity andnon-teratogenicity of test compounds are created using either Linearclassifiers (for e.g., partial least squares determinant analysis(PLS-DA), Fisher's linear discriminant, Logistic regression, Naive Bayesclassifier, Perceptron), Support vector machines (for e.g., leastsquares support vector machines), quadratic classifiers, Kernelestimation (for e.g., k-nearest neighbor), Boosting, Decision trees (fore.g., Random forests), Neural networks, Bayesian networks, Hidden Markovmodels, or Learning vector quantization.

In a preferred embodiment, the Random forest model is used to create aclassification model in order to predict teratogenicity andnon-teratogenicity of test compounds. Random forest (or random forests)is an ensemble classifier that consists of many decision trees andoutputs the class that is the mode of the class's output by individualtrees. A “decision tree” is a decision support tool that uses atree-like graph or model of decisions and their possible consequences,including chance event outcomes, resource costs, and utility. It is oneway to display an algorithm. Decision trees are commonly used inoperations research, specifically in decision analysis, to help identifya strategy most likely to reach a goal. Another use of decision trees isas a descriptive means for calculating conditional probabilities.Decision tree learning, used in statistics, data mining and machinelearning, uses a decision tree as a predictive model which mapsobservations about an item to conclusions about the item's target value.More descriptive names for such tree models are classification trees orregression trees. In these tree structures, leaves representclassifications and branches represent conjunctions of features thatlead to those classifications.

As used herein, a “training set” is a set of data used in various areasof information science to discover potentially predictive relationships.Training sets are used in artificial intelligence, machine learning,genetic programming, intelligent systems, and statistics. In all thesefields, a training set has much the same role and is often used inconjunction with a test set.

As used herein, a “test set” is a set of data used in various areas ofinformation science to assess the strength and utility of a predictiverelationship. Test sets are used in artificial intelligence, machinelearning, genetic programming, intelligent systems, and statistics. Inall these fields, a test set has much the same role.

As used herein, “regression analysis” includes any techniques formodelling and analyzing several variables, when the focus is on therelationship between a dependent variable and one or more independentvariables. More specifically, regression analysis helps understand howthe typical value of the dependent variable changes when any one of theindependent variables is varied, while the other independent variablesare held fixed. Most commonly, regression analysis estimates theconditional expectation of the dependent variable given the independentvariables—that is, the average value of the dependent variable when theindependent variables are held fixed. Less commonly, the focus is on aquantile, or other location parameter of the conditional distribution ofthe dependent variable given the independent variables. In all cases,the estimation target is a function of the independent variables calledthe regression function. In regression analysis, it is also of interestto characterize the variation of the dependent variable around theregression function, which can be described by a probabilitydistribution. Regression analysis is widely used for prediction andforecasting, where its use has substantial overlap with the field ofmachine learning. Regression analysis is also used to understand whichamong the independent variables are related to the dependent variable,and to explore the forms of these relationships. In restrictedcircumstances, regression analysis can be used to infer causalrelationships between the independent and dependent variables. A largebody of techniques for carrying out regression analysis has beendeveloped. Familiar methods such as linear regression and ordinary leastsquares regression are parametric, in that the regression function isdefined in terms of a finite number of unknown parameters that areestimated from the data. Nonparametric regression refers to techniquesthat allow the regression function to lie in a specified set offunctions, which may be infinite-dimensional.

“Sensitivity” and “specificity” are statistical measures of theperformance of a binary classification test. Sensitivity (also calledrecall rate in some fields) measures the proportion of actual positiveswhich are correctly identified as such (e.g. the percentage of sickpeople who are correctly identified as having the condition).Specificity measures the proportion of negatives which are correctlyidentified (e.g. the percentage of healthy people who are correctlyidentified as not having the condition). These two measures are closelyrelated to the concepts of type I and type II errors. A theoretical,optimal prediction can achieve 100% sensitivity (i.e. predict all peoplefrom the sick group as sick) and 100% specificity (i.e. not predictanyone from the healthy group as sick). A specificity of 100% means thatthe test recognizes all actual negatives—for example, in a test for acertain disease, all disease free people will be recognized as diseasefree. A sensitivity of 100% means that the test recognizes all actualpositives—for example, all sick people are recognized as being ill.Thus, in contrast to a high specificity test, negative results in a highsensitivity test are used to rule out the disease. A positive result ina high specificity test can confirm the presence of disease. However,from a theoretical point of view, a 100%-specific test standard can alsobe ascribed to a ‘bogus’ test kit whereby the test simply alwaysindicates negative. Therefore the specificity alone does not tell us howwell the test recognizes positive cases. A knowledge of sensitivity isalso required. For any test, there is usually a trade-off between themeasures. For example, in a diagnostic assay in which one is testing forpeople who have a certain condition, the assay may be set to overlook acertain percentage of sick people who are correctly identified as havingthe condition (low specificity), in order to reduce the risk of missingthe percentage of healthy people who are correctly identified as nothaving the condition (high sensitivity). This trade-off can berepresented graphically using a receiver operating characteristic (ROC)curve.

The “accuracy” of a measurement system is the degree of closeness ofmeasurements of a quantity to its actual (true) value. The “precision”of a measurement system, also called reproducibility or repeatability,is the degree to which repeated measurements under unchanged conditionsshow the same results. Although the two words can be synonymous incolloquial use, they are deliberately contrasted in the context of thescientific method. A measurement system can be accurate but not precise,precise but not accurate, neither, or both. For example, if anexperiment contains a systematic error, then increasing the sample sizegenerally increases precision but does not improve accuracy. Eliminatingthe systematic error improves accuracy but does not change precision.

The term “predictability” (also called banality) is the degree to whicha correct prediction or forecast of a system's state can be made eitherqualitatively or quantitatively. Perfect predictability implies strictdeterminism, but lack of predictability does not necessarily imply lackof determinism. Limitations on predictability could be caused by factorssuch as a lack of information or excessive complexity.

In one embodiment, the relative amounts of one or more biomarkerspresent in a first or second sample of a biological fluid aredetermined, in part, by executing an algorithm with a programmabledigital computer. The algorithm identifies at least one peak value inthe first mass spectrum and the second mass spectrum. The algorithm thencompares the signal strength of the peak value of the first massspectrum to the signal strength of the peak value of the second massspectrum of the mass spectrum. The relative signal strengths are anindication of the amount of the biomarker that is present in the firstand second samples. A standard containing a known amount of a biomarkercan be analyzed as the second sample to provide better quantify theamount of the biomarker present in the first sample. In certainembodiments, the identity of the biomarkers in the first and secondsample can also be determined

The basal metabolome of undifferentiated hSLCs serve as a collection ofbiochemical signatures of functional pathways that are relevant forstemness and self-renewal. Metabolite profiling can be conducted onexcreted, secreted or consumed or identified cellular metabolites asopposed to intracellular compounds. Ultimately, biomarkers discovered invitro are expected to be useful for analyzing in vivo biofluids thatcontain complex mixtures of extracellular biomolecules. Such biofluidsinclude but are not limited to serum, whole blood, plasma, sputum,cerebrospinal fluid, pleural fluid, amniotic fluid, urine and the like.This is advantageous over invasive procedures such as tissue biopsiesbecause small molecules in biofluids can be detected non-invasively (incontrast to intracellular compounds). In addition, processing cellularsupernatant for mass spectrometry is more robust and less laborious thancellular extracts. However, cellular extracts (from, for example, lysedcells) can be utilized in the methods of the invention.

The term “biomarker profile” as used herein refers to a plurality ofbiomarkers identified by the inventive methods. Biomarker profilesaccording to the invention can provide a molecular “fingerprint” of thetoxic and teratogenic effects of a test compound and convey whatcellular metabolites, specifically excreted and secreted cellularmetabolites, are significantly altered following test compoundadministration to hSLCs. In these embodiments, each of the plurality ofbiomarkers is characterized and identified by its unique molecular massand consistency with which the biomarker is detected in response to aparticular toxic, teratogenic or test chemical compound; thus the actualidentity of the underlying compound that corresponds to the biomarker isnot required for the practice of this invention.

The term “biomarker portfolio” as used herein refers to a collection ofindividual biomarker profiles. The biomarker portfolios can be used asreferences to compare biomarker profiles from novel or unknowncompounds. Biomarker portfolios can be used for identifying commonpathways, particularly metabolic or developmental pathways, of toxic orteratogenic response.

The results set forth herein demonstrate that hSLC metabolomics can beused in biomarker discovery and pathway identification. Metabolomicsdetected small molecules secreted or excreted by hSLCs, consumed byhSLCs, or the flux of metabolites through hSLCs. The identifiedbiomarkers can be used for at least two purposes: first, to determinespecific metabolic or biochemical pathways or networks that respond toor are affected by toxin or teratogen exposure, particularly saidpathways utilized or affected during early development that aresensitive to toxic, teratogenic or test chemical compounds that aredevelopmental disruptors and participate in the ontogenesis of birthdefects; and second, to provide metabolites that can be measured inbiofluids to assist management and diagnosis of toxic exposure, birthdefects or other disease.

In one embodiment, the metabolites of a biomarker portfolio are mappedto one or more metabolic networks in order to determine keydevelopmental pathways affected by a test compound. In one aspect,online databases are used to map the metabolites to one or moredevelopmental pathways. These online databases include, but are notlimited to, HMDB, KEGG, PubChem Compound, and METLIN. In anotherembodiment, one or more developmental processes associated with the oneor more metabolic networks are identified in order to determine one ormore developmental processes or pathways disrupted by a test compound.

In a further embodiment, the potential specific effect of a teratogeniccompound can be identified with further consideration. Specifically, byway of example, it is known that certain developmental or biologicaldefects are correlated to disruptions in one or more metabolic networks,and by not just identifying the existence of the metabolites affected bythe disruption of these metabolic networks, but further comparing theaffected metabolites to their normal metabolic network profiles, aperson of skill in the art would be able to correlate the specificeffect of the teratogenic compound to its potential specific biologicaleffect on a patient. This type of information helps to elucidatespecific developmental pathways that may be affected by exposure to ateratogenic compound.

A biomarker portfolio from hSLCs can also serve as a high throughputscreening tool in preclinical phases of drug discovery. In addition,this approach can be used to detect detrimental effects of environmental(heavy metals, industrial waste products) and nutritional chemicals(such as alcohol) on human development. Ultimately, the methods of thisinvention utilizing the hSLC metabolome can assist pharmaceutical,biotechnology and environmental agencies on decision-making towardsdevelopment of compounds and critical doses for human exposure. Theintegration of chemical biology to embryonic stem cell technology alsooffers unique opportunities to strengthen understanding of humandevelopment and disease. Metabolomics of cells differentiated from hSLCsshould serve similar roles and be useful for elucidating mechanisms oftoxicity and disease with greater sensitivity for particular cell ortissue types, and in a human-specific manner.

For example, key metabolic networks, including as set forth hereinarginine, aspartic acid, gamma aminobutyric acid (GABA), glutamate andisoleucine synthesis and degradation, may be differentially disrupted inearlier versus later stages of human development. In addition,metabolite profiles of neural precursor cells or neuronal cellpopulations can reveal biomarkers of neurodevelopmental disorders intarget cell types. The association of metabolomics to stem cell biologycan inform the mechanisms of action of folic acid and neural tubedefects in the early human embryo.

Biomarker portfolios produced using the hSLC-dependent methods of thisinvention can also be used in high throughput screening methods forpreclinical assessment of drug candidates and lead compounds in drugdiscovery. This aspect of the inventive methods produces minimal impacton industry resources in comparison to current developmental toxicologymodels, since implementation of this technology does not requireexperimental animals. The resulting positive impact on productivityenables research teams in the pharmaceutical industry to select andadvance compounds into exploratory development with greater confidenceand decreased risk of encountering adverse developmental effects.

The term “developmental pathway” or “developmental process” or“developmental network” as used herein refers to biochemical ormetabolic networks involved in embryonic and fetal development.

“Supernatant” as used herein can include but is not limited toextracellular media, co-cultured media, cells, or a solution offractionated or lysed cells.

Metabolite profiles obtained from analysis of toxins, teratogens,alcohol, and test chemical compounds can be used to compose a library ofbiomarker portfolios. These portfolios can then be used as a referencefor toxicological analysis of unknown chemical compounds. Metabolicprofiles of novel compounds can be compared to known biomarkerportfolios to identify common mechanisms of toxic response. Thisapproach can reveal functional markers of toxic response, which serve asscreening molecules that are shared at least in part as a consequence ofexposure to various different toxic and teratogenic compounds. SuchhSLC-derived small molecules can be used as measurable mediators oftoxic response that refine or replace costly and complex screeningsystems (such as in vivo animal models) and have the additionaladvantage of being specific for human cells and human metabolic anddevelopmental pathways.

Kits

As a matter of convenience, the method of this invention can be providedin the form of a kit. Such a kit is a packaged combination comprisingthe basic elements of: a) a first container comprising, in solid form, aspecific set of purified metabolites having a molecular weight of lessthan about 3000 Daltons, wherein a difference in the specific set ofpurified metabolites associated with hSLCs cultured in the presence ofknown teratogenic compounds versus hSLCs cultured in the absence ofknown teratogenic compounds indicates a difference in metabolic responseof hSLCs cultured in the presence of the known teratogenic compounds incomparison with hSLCs cultured in the absence the known teratogeniccompounds; and b) a second container comprising a physiologicallysuitable buffer for resuspending the specific subset of purifiedmetabolites.

In one embodiment, the kit can further include an instruction sheet,describing how to carry out the assay of the kit.

In another embodiment, the kit can also encompass one or more reagentsto analyze fluctuations of expression and/or activity of one or moreenzymes which are involved in the endogenous biological reactions whichresult in the synthesis and/or conversion of one or more metabolitesdisclosed herein. Thus, the kit is not limited to the analysis anddetection of small molecule biomarkers, but also of the enzymes whichare inherent components of the metabolic neworks described herein. Inone embodiment, analysis of enzyme activity and/or concentration in thekit, as an indicator of metabolite changes can be performed by assaysincluding but not limited to gene expression analysis, ELISA and otherimmunoassays as well as enzyme substrate conversion.

In another embodiment, the invention discloses a method for validating atest compound as a teratogen. In one embodiment, the method comprisesproviding a set of metabolites having a molecular weight of less thanabout 3000 Daltons. In one aspect, the metabolites are provided in thesame container. Ina another aspect, each metabolite is provided in aseparate container. In one aspect, the metabolites are differentiallymetabolized by hSLCs cultured in the presence of one or more knownteratogenic compounds in comparison with hSLCs cultured in the absenceof a teratogenic compound. In one aspect, the metabolitres are providedin a solid form. In another aspect, the metabolites are provided in aliquid form. Thus, in one embodiment, the method comprises resuspendingthe set of metabolites. In one aspect, the metabolites are resuspendedin a buffer. In another aspect, metabolites are resuspended in anysuitable liquid. In another aspect, the buffer is a physiologicallysuitable buffer. In one aspect, the metabolites are resuspended in apredetermined volume of the buffer. In another aspect, the finalconcentration of each metabolite in the buffer is identical to theconcentration of that metabolite associated with hSLCs cultured in thepresence of one or more known teratogenic compounds. In anotherembodiment, the method comprises generating a reference profile of themetabolites by method disclosed herein. In yet another embodiment, themethod comprises comparing a profile of mass features generated uponexposure of hSLCs to the test compound with the reference profile ofmetabolites in order to validate the teratogenicity of the testcompound.

Advantages of a hSLC Developmental Toxicity Prediction Model

The hSLC-based assay reported herein has several distinct advantagesover other standard approaches, namely: 1) Alterations to themetabolites in response to a toxicant is a sensitive and quantitativemeasurement, which enables more objective data-driven decisions. 2)Multiple biochemical pathways can be assessed simultaneously, whichreinforces the robustness of the model when applied to drugs with avariety of mechanisms of toxicity. 3) Metabolic endpoints are a measureof functional biochemical pathways that can be rapidly integrated withprotein, DNA, and RNA targets for further pathway-based investigation.4) Because the prediction is based on multiple independent variables, itis possible to detect teratogens exhibiting complex changes in metabolicpatterns. 5) The assay is independent of cell death outcomes and istrained on circulating doses known to cause human developmentaltoxicity, which increases the probability of finding developmentaltoxicants that are not just toxic to dividing cells. 6) Testing andanalysis is higher throughput, less labor intensive and automatable.

Comparison of hSLC Developmental Toxicity Prediction Model to OtherModels

Developmental toxicity testing in cells derived from human embryos ishighly likely to generate more reliable in vitro prediction endpointsthan those currently available through the use of animal models, orother in vitro non-human assays such as zebra fish models, the EST, andwhole embryo culture (WEC) given the physiological relevance of hSLC tohuman development.

The hSLC model has important biological features in comparison tozebrafish assay systems. First, it is a human system, providing speciesspecificity to predict human outcomes. Zebrafish developmental andbiochemical pathways can be quite distinct from those that are criticalto human development, for example the absence of placentation andpulmonary differentiation and development, as well as differentmechanisms for cardiogenesis. Moreover, the screening throughput ofzebrafish assays is somewhat limited due to the high degree ofdevelopmental defects associated with small well size (Selderslaghs etal. 2009). The fish are also sensitive to very low concentrations ofDMSO, where levels greater than 0.25% cause increased deformities. Thedetermination of a specific defect, by visual inspection of changes inmorphology, can also be highly subjective while perturbation to theabundance of small molecule metabolites is a quantitative endpointmeasured by a highly sensitive analytical chemistry technique(LC-ESI-QTOF-MS).

TABLE 1 Accuracy of Developmental Toxicity Models Model # Drugs AccuracyZebra Fish (McGrath 2008) 12 91 devTOX (hSLCs) 8 88 EST (Paquette 2008)63 83 WEC (Genschow 2002) 14 80 EST (Genschow 2002) 20 78 Zebra FishEmbryos (Chapin 2008) 18 72 MM (Genschow 2002) 20 70 WEC (Genschow 2002)14 68

In comparison to those reported for the EST, which measures cytotoxicityand the ability of chemicals to disrupt proper differentiation of mEScells into cardiomyocytes, the overall reliability of the hSLC assayreported here, based on a metabolic signature of toxicity, was superiorto the EST. The EST predictive model is strongly correlated withcytotoxicity, given that two EST variables result from the IC50concentrations observed in fibroblasts compared to mES cells. Thesevariables make the assumption that developmental toxicants cause celldeath at lower concentrations in embryonic cells compared to the “adult”fibroblast cells, which may not be valid for many mechanisms of toxicity(for example—Thalidomide). The dose required to reach an IC50 may alsobe much higher than the typical circulating dose or that which may beencountered by the fetus in utero leading to large numbers of falsepositives. It is also likely that changes in cell viability may beobserved in vitro which will not occur in vivo.

Further, the hSLC based assay correctly classifies thalidomide as ateratogen while the EST does not (Nieden et al. 2001). The hSLC model isalso considerably more predictive than either WEC or micro mass (MM)(Table 1). Further, the hSLC and metabolomics based model offers anopportunity to understand the mechanisms of developmental toxicity in anall human system.

In one embodiment, a virtual library containing all the biomarkersdiscovered in this study can be established. Such a library provides arepository of human biomarkers useful in assessing developmentaltoxicity, not only of pharmaceutical agents, but also of otherchemicals, the latter subject to increased attention from regulatorydirectives, namely REACH, in Europe. By integrating a larger number ofpharmaceutical compounds in addition to other chemicals that are knownto disrupt human development (such as chlorpyrifos, organophosphates,methylmercury) one can further expand the biomarker library and therobustness of metabolomics biomarkers across very diverse collections ofchemicals. Although exemplified in a six-well format, metabolomics ofhSLCs in a 96-well format are contemplated to enable high-throughputscreening of chemical collections such as those available at theMolecular Libraries Program (NIH) or NTP (National Toxicology Program,NIEHS). In addition, a targeted metabolomics approach employing the useof triple quadrupole MS for ultra fast, sensitive and more specificquantitation of metabolites is expected to improve throughput.

The present invention illustrates the ability to utilize hSLCs andmetabolomics to provide a predictive, quantitative, all-human in vitroscreening method for predicting developmental toxicity of compounds. Themodel also provides the opportunity to investigate mechanisms oftoxicity of compounds by studying the metabolite response of hSLCsexposed to those compounds. Thus, this method has the potential to aidin the prevention of birth defects induced by chemical compounds and toreduce animal testing.

In one embodiment, the present invention provides a more predictive invitro assay than those currently available in order to further identifybiomarkers that are specific to humans, rather than to rodents or othernon-human biological systems. Therefore, in one embodiment, theinvention provides assays that are more accurate, sensitive, and/orspecific than available assays.

In one embodiment, the invention discloses a method for predicting theteratogenicity of a test compound with at least about 80% accuracy, andmore particularly with at least about 85% accuracy. In preferredembodiments, the invention discloses a method for predicting theteratogenicity of a test compound with at least about 90% accuracy.

In another embodiment, the invention discloses a method for predictingthe teratogenicity of a test compound with at least about 80%sensitivity, more particularly with at least about 85% sensitivity, andeven more particularly with at least about 95% sensitivity.

In still another embodiment, the invention discloses a method forpredicting the teratogenicity of a test compound with at least about 80%specificity, and more particularly with at least about 85% specificity.In preferred embodiments, the invention discloses a method forpredicting the teratogenicity of a test compound with at least about 95%specificity.

In one embodiment, the invention uses a machine learning model todevelop a highly accurate, sensitive, and specific assay to determineteratogenicity of test compounds. Accordingly, in one embodiment, theinvention provides an initial training set of known teratogenic andnon-teratogenic compounds to dose hSLCs. In another embodiment, theinvention adds a test compound identified as a teratogen to the initialtraining set to obtain an expanded training set. In one embodiment, theexpanded training set allows for a more accurate, sensitive, andspecific model for predicting teratogenicity of test compounds.

In one embodiment, dosing compounds were dosed at concentrationscorresponding to their IC50 or EC50 dose levels. In another embodimentdosing compounds were dosed at concentrations corresponding to two dosesbelow their IC50 or EC50 dose levels. In another embodiment, dosingcompounds were dosed at concentrations corresponding to theircirculating dose. In one aspect, dosing compounds at concentrationscorresponding to their circulating dose recapitulates the exposure levelto a developing human embryo in vivo and the toxic or teratogenic effectof the dosing compound on human development.

In one embodiment, determination of teratogenicity of a test compoundinvolves comparing the metabolic response of hSLCs cultured in thepresence of a test compound with the metabolic response of hSLCscultured in the absence of the test compound. In another embodiment,determination of teratogenicity of a test compound involves comparingthe metabolic response of hSLCs cultured in the presence of a testcompound with the metabolic response of hSLCs cultured in the presenceof a known non-teratogenic compound. In one aspect, the comparison ofmetabolic response of hSLCs cultured in the presence of a test compoundwith the metabolicresponse of hSLCs cultured in the presence of a knownnon-teratogenic compound allows for a more specific, sensitive, andaccurate assay to predict teratogenicity of a test compound. In oneembodiment, a non-teratogenic compound is any compound that, uponexposure to hSLCs, does not alter the normal metabolism of hSLCs.Examples of non-teratogenic compounds or agents include, but are notlimited to, sugars, fatty acids, spermicides, acetaminophens, prenatalvitamins, and the like.

EXAMPLES

The Examples which follow are illustrative of specific embodiments ofthe invention, and various uses thereof. They are set forth forexplanatory purposes only, and are not to be taken as limiting theinvention.

Example 1 hES Cell Culture

WA09 hESCs, obtained from WiCell Research Institute (NIH National StemCell Bank, Madison, Wis.) were cultured in 6-well plates on Matrigel (BDBiosciences, San Jose, Calif.), in mTeSR1 medium (Stem CellTechnologies, Vancouver, BC) incubated at 37° C. under 5% CO2 in aThermo Electron Form a Series II Water Jacket CO2 Incubator. hESCs werepassaged every three or four days at a 1:3 or 1:6 seeding density forroutine culture conditions. For dosing experiments, hESCs were passagedat a low density of 1:10 or 1:12 so that they would not requirepassaging during the seven-day dosing protocol. To passage hES cells,the StemPro® EZPassage™ disposable stem cell passaging tool (Invitrogen,Carlsbad, Calif.) was used to detach the cells from the wells. Detachedcells were removed with a pipette and distributed to new Matrigelplates.

Example 2 hES Cell Dosing

A training set of established teratogens and non-teratogens (Table 2)was used to dose hESCs. The training set is a collection of chemicalstandards that includes compounds that had been previously used inmulticenter efforts aimed at developing and validating novelalternatives to predict developmental toxicity, such as the EST,proposed by the ECVAM agency.

All tested chemicals were purchased from Sigma-Aldrich (St. Louis, Mo.).Cells were dosed with drugs at a concentration equivalent to theirpublished serum circulating therapeutic dosages. Dosing was performed onhESCs in 6-well plates in triplicate, i.e. three wells per plate. Theplates were dosed in triplicate, so there were a total of nine dosedwells. In parallel, there were nine “control” wells, in which hESCs werecultured with mTeSR1 containing no drug, and three wells containingMatrigel with mTeSR1 medium without hESCs that served as mediumcontrols. Lastly, three wells of dosed medium controls were prepared,containing Matrigel, mTeSR1 and drug, but no hESCs (FIG. 1). Thesemedium controls provided baseline mass spectral data. On the first dayof dosage, the determined concentration of drug was dissolved in mTeSR1,and then 2.5 mL of this solution was added to each dosed well of hESCs.Each day, for four days, the medium was removed and new dosed medium wasadded. On the fourth day, the medium was removed and added toacetonitrile to make a 40% acetonitrile solution, as outlined in theSample Preparation section below.

Since it is the goal of the present study to develop a more predictivein vitro assay than those currently available, and to further identifybiomarkers that are specific to humans, rather than to rodents or othernon-human biological systems, the ECVAM test set was replicated in thisstudy. Additional drugs were included in the training set to increasethe number of the non-teratogen chemicals, as well as to supplement thestrong teratogens.

TABLE 2 Chemical compounds in the training and test set (blinds) usedfor dosing, their classification according to teratogenicity andprediction model incorporation. TS1 and TS2 indicate Training Set 1 and2 respectively. Stemina Model ECVAM Classification Compound Training SetClassification Non- Ascorbic Acid TS1, 2 Non-Teratogens TeratogensDoxylamine (Blind 2) TS2 Isoniazid TS1, 2 Levothyroxine TS1, 2Penicillin G TS1, 2 Folic Acid TS1, 2 Retinol (Blind 1) TS2 Thiamine(Blind 8) TS2 Aspirin TS2 Weak/Moderate Caffeine TS2 TeratogensDexamethasone Diphenhydramine TS2 Teratogens Diphenylhydantoin TS2Methotrexate TS2 5-Fluorouracil TS1, 2 Strong Teratogens Accutane (Blind6) TS2 Amiodarone (Blind 3) TS2 Busulfan TS1, 2 Carbamazepine TS2 (Blind5) Cyclophosphamide TS2 (Blind 7) Cytosine Arabinoside TS1, 2Hydroxyurea TS1, 2 Retinoic Acid TS1, 2 Rifampicin (Blind 4) TS2Thalidomide TS1, 2 Valproic Acid TS1, 2

Compounds were dosed at concentrations corresponding to theircirculating dose rather than IC50 or EC50 dose levels. Dosing was doneat the circulating maternal dose as published in the literature in aneffort to recapitulate the exposure level to the developing human embryoin vivo and the toxic effect on human development rather than creating amodel which measures toxic effect on hESCs in culture. It is noteworthyto mention that the substances employed in this screen (the ECVAM testset) exert their developmental toxicity in a manner that is independentof maternal metabolism.

In other words, this test set was established and employed inmulticenter, randomized trials due to the fact that the parent compound,and not reactive metabolites, impair proper human development and arethus suitable to develop novel means for in vitro screening.

Example 3 hES Cell Viability Assays

In addition to determining teratogenicity by molecular endpoints, withmetabolomics, cell viability was examined using a subset of the drugs todetermine if a correlation exists between cell death and compoundteratogenicity. In particular, the viability assay was conducted toaddress the concern that the metabolic endpoints may be stronglycorrelated with cell death rather than developmental toxicity sincedosing with the antineoplastic drugs cytosine arabinoside and5-fluorouracil often resulted in the most profound changes in manymetabolites.

Cell viability assessment in response to exposure to chemical compoundswas examined using the MultiTox-Fluor Assay (Promega, Madison, Wis.),which simultaneously measures cell viability and cytotoxicity. WA09hESCs were seeded at a density of 250,000 cells/well in a 96-well plate.Cells were fed with dosed media daily, for four days. On the fourth day,spent medium was removed, 100 μL of fresh medium was added along with100 μL of the MultiTox-Fluor reagent. The plate was incubated at 37° C.,5% CO2 for 30 minutes and measured. The ratios of live to dead cellswere normalized to the control cells (no treatment) in order to reportrelative cell viability.

Cell viability data (FIG. 2) showed no discernable correlation betweenteratogenicity and cell death relative to control cells. Thus,therapeutic concentrations of teratogens are not correlated with celldeath in a significant manner, despite the evidence of statisticallysignificant metabolomic changes. This finding suggests that metabolomicshas a lower threshold, or increased sensitivity to detect molecularchanges associated with developmental toxicity and specific biomarkersin comparison to standard cell death assays, which should provide a morepredictive and sensitive screen for developmental toxicity.

Example 4 Developmental Toxicology Screening

Sample Preparation

The 2.5 mL of spent media per well from Example 1 was added to 1.67 mLacetonitrile to make a 40% acetonitrile solution. The acetonitrile actsto “quench” the spent media sample, slowing or halting many metabolicprocesses and aiding in precipitation of cellular proteins. Samples wereeither stored at −80° C. for later analysis, or for immediate analysis,250 μL of the quenched solution was mixed with 250 μL of water, to afinal concentration of 20% acetonitrile, then added to a 3 kDa molecularweight cut-off filter spin column (Microcon YM-3 Centrifugal Filter,Millipore, Billerica, Mass.). Each sample was then centrifuged in an IECCL31R Multispeed Centrifuge (Thermo Scientific, Waltham, Mass.) at13,000×g at 4° C. for 200 minutes. Following centrifugation, theflow-through was saved then dried for several hours in a Savant HighCapacity Speedvac Plus Concentrator. The concentrated sample was thendissolved in 50 μL of 0.1% formic acid prior to LC-MS analysis.

Mass Spectrometry

Mass spectrometry was performed using an Agilent QTOF LC/MS systemconsisting of a G6520 AA QTOF high resolution mass spectrometer capableof exact mass MS and MS/MS. In order to facilitate separation of smallmolecules with a wide range of polarity and to allow increased retentionof hydrophilic species, Hydrophilic Interaction Liquid Chromatography(Alpert 1990) was employed. Each sample was run for 30 minutes with thegradient shown in Table 3 at a flow rate of 0.5 mL/min, using 0.1%formic acid in water (Solvent A) and 0.1% formic acid in acetonitrile(Solvent B). Electrospray ionization was employed using a dual ESIsource, with an Agilent isocratic pump continuously delivering aninternal mass reference solution into the source at approx. 0.01 mL/min.The mass range of the instrument was set to 100-1700 Da. A PhenomenexLuna HILIC column with dimensions 3×100 mm 3 μm particle size was usedand maintained at 30° C. 5 μL of each sample was injected. Dataacquisition was performed with Agilent MassHunter using high-resolutionexact mass conditions.

TABLE 3 HILIC gradient % B % A Acetonitrile with 0.1% Time (min) 0.1%formic acid (aq) formic acid 0.0 5 95 1.5 5 95 16.0 40 60 17.0 95 5 21.095 5 22.0 5 95 30.0 5 95Mass Spectral Data Preprocessing

Following LC-MS, chromatograms were inspected for reproducibility. LC-MSruns with total ion counts that vary by more than 25% were repeated toensure that samples could be accurately compared. These runs were thenused to create mass features that correspond to molecules detectedacross the different LC-MS runs. Mass features were extracted from theLC-MS data using MassHunter Qualitative Analysis software (AgilentTechnologies). The following criteria were used as general guidelines,however some flexibility and optimization was needed. m/z values withinthe range of 75-1500, with a charge of +1 or −1, and a centroid heightgreater than 1000 were used to generate “mass features.” The mass peaksthat pass these criteria were used to fit isotope and adduct (Na⁺, K⁺,and NH4⁺) patterns corresponding to individual molecules, and toestablish the abundance of each mass feature. The abundance iscalculated by MassHunter software as the sum of the isotopic and adductpeaks that correspond to a single molecular feature. After datadeconvolution, mass features showing at least two ions (e.g. (M⁺H)⁺ and(M⁺H)⁺⁺1 or (M⁺H)⁺ and (M⁺Na)⁺) and an abundance value greater than50000 for positive-ion mode data and 10000 in negative-ion mode datawere included in the data set used for binning of the mass features.

Following feature selection by MassHunter, the data was furtherpreprocessed by MassProfiler (Agilent) software which aligns massfeatures across multiple LC-MS data files. Mass features were generatedfor data from each drug treatment experiment (dosed and control) usingthe default alignment settings in MassProfiler with the requirement thata feature be present in at least 80% of the samples in one treatment.The mass feature datasets for each drug treatment experiment werefurther processed in a global manner using custom analysis scriptsexecuted in the R statistical software environment.

Files for each drug experiment were binned using an algorithm based onboth exact mass and retention time in order to consider a mass featurethe same across different LC/ESI-MS-QTOF runs. The binning criteria isbased on both a sliding mass difference scale that allows for largermass differences at lower molecular weights and a constant retentiontime window based on the reproducibility of the chromatography. Masseswere ordered and considered to be the same feature if a mass under 175Da differs by less than 18 ppm from the previous mass, while masses176-300 Da were binned by 12 ppm, and 10 ppm when over 300 Da. Thesemass bins were ordered by retention time and if a difference inretention of the previous feature was less than twelve seconds it wasconsidered to be the same feature across LC-MS runs. The binning processis used to create unique compound identities (cpdID) that are assumed torepresent a single small molecule. If multiple features appeared to fallinto the same bin their abundances were averaged.

Determination of Metabolic Flux, Secreted, Excreted, Consumed, orIdentified Metabolites

The media represents a major factor in the experimental system, in thatit contributes many peaks to mass spectra. This can be accounted for ina data dependent manner to select for mass feature bins, which arepresent at significant levels above the media. Mass feature bins presentsolely in the presence of cells (not detected in media) or with averageabundance levels different than uncultured media were considered to besecreted, excreted, consumed, or identified metabolites.

Validation of Small Molecule Metabolites

In validating the identities of specific metabolites, three criteriawere used: 1) The exact mass of the metabolite must be within 10 ppm ofthe known mass of the compound. 2) The retention time of the metabolitedetected in the cell media must be within + or −30 seconds of areference standard on which MS data had been acquired under the sameconditions. The reference standards were dissolved in mTeSR media andprepared in exactly the same manner (described above) as the samplesfrom the cells, including the addition of acetonitrile, Centriconcentrifugal filtration, drying then dissolution in formic acid prior toLC-MS analysis. 3) The MS-MS fragmentation spectra of the metabolitedetected in the cell media must be a reasonable match with that of thereference standard, including abundances and m/z values of the fragmentions. If published MS-MS spectra are available, the MS-MS spectra mustalso be a reasonable match.

Example 5 The Random Forest Model

Teratogen Classification for the Random Forest Model

The classification of teratogenicity in previously published animal andcell culture models of developmental toxicity were trained using threedifferent classes, non-teratogens, weak/moderate teratogens, and strongteratogens, based largely on embryotoxicity outcomes and developmentalabnormalities observed in animal models (Marx-Stoelting et al. 2009,Chapin et al. 2008). In the present study a modified approach tocompound classification since there are many species specificdifferences in developmental toxicity, focusing the compoundteratogenicity classification strictly on observed human risk associatedwith each chemical.

Thus, the criteria of observed human teratogenicity risk led to a modelwith two categories of toxicity, teratogen or non-teratogen, whichaccurately reflects the ultimate intended outcome of the predictivemodel. This also reduces technical challenges associated with attemptingto determine the potency of teratogens based on distantly relatedspecies. Additionally, such a focused classification schema (teratogensversus non-teratogens) leads to a more robust and predictive metabolicmodel of human developmental toxicity given the limited availability ofreliable, quantitative data of human risk associated with exposure toweak or moderate teratogens.

Random Forest Modeling

Random Forest (Brieman 2001) was used to create a classification modelin order to predict teratogenicity and non-teratogenicity using themedian fold change of drug treatment versus its intra-experimentalcontrol for each feature (variable) included in the model. Bagging wasperformed on ⅓ of the samples by re-sampling with replacement 1000bootstrap subsets from the training set data of known teratogens. Finalprediction from the RF classifier on the blinded drugs was based on themajority vote of the ensemble of trees.

Feature Set Used for Random Forest Modeling

The dataset utilized for random forest modeling was a subset of highquality reproducible features. Features were selected if they had valuespresent in at least 75% of the drug treatment experiments (blind andknown drugs). This list of features was then filtered against a list ofknown contaminant molecules such as HEPES and PEG and their numerousadducts to remove features of non-biological interest. Finally, featureswith poor binning or grouping characteristics were removed.

Feature selection by variable importance was performed by selectingfeatures with a mean decrease in accuracy greater than 0.5. Randomforest based analysis was executed using the Random Forest library (Liaw& Wiener 2002). Model metrics were calculated based on the resultingrandom forest confusion matrix or the predictions of blinded drugs usingthe methods outlined in (Genschow et al. 2000).

The abundance values were then log base two transformed and the medianvalue of each treatment (dosed and control) within each experiment(different drugs) was used. The data was then normalized by control foreach drug treatment experiment. The resulting median log fold changevalues were used as the input data values for the random forestmodeling. Missing median fold change data was replaced with a 0. Theremaining positive and negative ESI mode features were combined creatinga dataset with 142 features used for modeling.

Example 6 Random Forest Model Results

As discussed in Example 5, random forest model was trained using afiltered dataset consisting of reproducibly measured mass features fromboth ESI polarities. The median fold change value of a mass feature forthe replicates for each drug versus its associated intra-experimentalcontrol were used as the variables to predict the teratogenicity ofdrugs.

The initial training set (TS1) contained 142 mass features resultingfrom exposure of hESCs to seven teratogens and five non-teratogens (seeTable 2).

The detailed annotations for the 142 mass features is provided in Table4. In comparing the retention times (RT) and mass averages (MASSavg) ofeach of the mass features with masses recorded in databases such asKegg, Metlin, HMDB, CAS, PUBCHEMS, PUBCHEMC, CHEBI etc., one will findtypically one or more putative candidate metabolites for each massfeature that match the retention times and mass averages. During thesubsequent validation process, the identity of the metabolitecorresponding to a specific retention time and mass average isdetermined The metabolite identities validated thus far are alsoprovided in Table 4.

TABLE 4 Feature Table Summary newID ESImode RT MASSavg MetaboliteNEGM102T150 NEG 150 102.0317 neg11 neg 443 103.0631 Gamma- Aminobutyricacid neg12 neg 78 104.0473 neg15 neg 504 105.9670 NEGM116T150 NEG 150116.0110 NEGM116T90 NEG 90 116.0110 Fumaric acid neg71 neg 81 118.0265Succinic acid neg73 neg 76 118.0626 Succinic acid neg94 neg 655 121.0202neg101 neg 103 129.0427 neg105 neg 103 129.1420 neg133 neg 75 132.0779Hydroxyisocaproic acid ESIneg.M132T451 NEG 451 133.0375 Aspartic AcidNEGM134T120 NEG 120 134.0215 Malic acid neg158 neg 71 139.0626NEGM147T450 NEG 450 147.0532 L-Glutamic acid neg198 neg 503 149.9571NEGM155T288 NEG 288 155.0695 neg275 neg 441 169.0339 neg295 neg 320173.0816 NEGM174T505 NEG 505 174.1117 L-Arginine neg360 neg 438 187.0445neg360 NEG 445 187.0453 neg414 neg 496 200.0279 neg429 neg 436 203.0548neg431 neg 80 203.1149 neg435 neg 506 204.0192 ESIneg.M215T293 NEG 293216.0391 neg559 neg 445 231.0737 neg563 neg 73 231.9462 neg622 neg 655240.0229 L-Cystine neg763 neg 441 260.0964 neg776 neg 655 262.0048neg779 neg 504 263.9399 neg811 neg 320 267.0687 neg831 neg 435 271.0422neg840 neg 502 273.9691 neg1095 neg 73 325.9324 neg1112 neg 495 328.0276neg1121 neg 55 331.0842 neg1136 neg 90 333.1022 neg1149 neg 494 336.0678neg1167 neg 435 339.0297 neg1192 neg 493 346.0948 neg1264 neg 487360.1113 neg1366 neg 441 379.1370 neg1433 neg 448 393.1215 neg1458 neg73 401.8984 neg1491 neg 434 407.0167 neg1568 neg 82 426.0714 neg1700 neg83 453.2425 neg1728 neg 485 460.1752 neg1787 neg 434 475.0041 neg1932neg 654 502.0281 neg2068 neg 482 528.1597 neg2115 neg 434 542.9914neg2355 neg 434 610.9786 neg2606 neg 434 678.9659 neg3446 neg 881051.0609 neg3535 neg 73 1110.9571 pos48 pos 55 103.2278 pos69 pos 661105.9789 pos102 pos 80 113.0843 pos102 POS 81 113.0844 pos134 pos 345117.0787 L-Valine pos136 pos 54 117.1150 pos213 pos 43 129.1513POSM131T330 POS 330 131.0946 L-Isoleucine pos368 pos 90 150.0898 PEG (n= 3) POSM155T288 POS 288 155.0695 pos422 pos 83 156.1256 pos446 pos 79158.1412 pos477 pos 439 164.0466 pos518 pos 79 169.0740 Pyridoxinepos525 pos 89 170.0574 pos529 pos 72 170.1416 POSM174T505 POS 505174.1117 L-Arginine pos563 pos 503 174.2275 pos593 pos 44 177.1264pos625 pos 511 181.9569 pos628 pos 42 182.1780 pos681 pos 133 188.1885pos687 pos 46 189.1261 pos687 POS 48 189.1264 pos698 pos 441 191.0167pos744 pos 70 198.1728 POSM202T432 POS 432 202.1430 AsymmetricDimethyl-L-arginine pos892 pos 88 219.1119 Pantothenic Acid pos917 pos640 222.0666 L-Cystathionine pos922 pos 511 222.9821 pos962 pos 503227.9989 pos970 pos 43 229.2402 pos1062 pos 655 240.0245 pos1084 pos 50242.1753 pos1095 pos 96 244.0927 pos1113 pos 493 246.0704ESIpos.M286T667 POS 667 285.0961 pos1471 pos 98 288.1188 pos1668 pos 437312.0289 pos1684 pos 485 314.1091 pos1698 pos 42 315.2042 pos1698 POS 47315.2050 pos1734 pos 597 320.0177 pos1773 pos 484 325.5943 pos1791 pos506 328.0611 pos1820 pos 600 331.8777 pos1896 pos 495 342.1509 pos1975pos 257 354.0566 Phenol Red pos2019 pos 434 361.0133 pos2094 pos 483372.1079 pos2109 pos 498 374.0676 pos2178 pos 519 384.2048 pos2225 pos438 393.0924 pos2284 pos 462 401.2065 pos2288 pos 688 402.0769 pos2489pos 258 429.9682 pos2512 pos 69 434.1609 pos2527 pos 82 436.2287 pos2634pos 83 452.2026 pos2693 pos 67 462.1915 pos2763 pos 771 474.0917 pos2786pos 81 476.2248 pos2814 pos 655 478.1241 pos2823 pos 80 480.2554 pos3023pos 495 510.1957 pos3095 pos 83 524.2812 pos3183 pos 82 540.2552 pos3308pos 82 564.2780 pos3425 pos 83 584.2815 pos3627 pos 83 624.2771 pos3675pos 77 633.3299 pos3728 pos 91 640.8206 pos3777 pos 82 652.3312 pos3795pos 492 656.2567 pos3832 pos 92 662.8337 pos3871 pos 77 669.3682 pos3912pos 625 677.2845 pos3978 pos 91 692.3328 pos3980 pos 91 692.8341 pos4039pos 93 706.3583 pos4063 pos 83 712.3292 pos4079 pos 91 714.8472 pos4136pos 93 728.3714 pos4178 pos 91 736.3593 pos4208 pos 81 740.3842 pos4250pos 91 750.3846 pos4267 pos 83 756.3554 pos4283 pos 91 758.8738 pos4340pos 91 772.3978 pos4343 pos 89 772.8994 pos4371 pos 91 780.3856 pos4435pos 90 794.4109 pos4438 pos 90 794.9130 pos4513 pos 89 816.4240 pos4515pos 90 816.9258 pos4543 pos 91 824.4121 pos4559 pos 79 828.4363 pos4594pos 90 838.4372 pos4596 pos 90 838.9392 pos4617 pos 83 844.4077 newID:Stemina in house name for mass feature. This is a designation for eachmetabolite/mass feature produced during analysis. It designates a uniquefeature; ESImode: electrospray ionization mode feature was detected in;RT: average retention as measured across ~1000 LC-MS runs of massfeature; MASSavg: average neutral mass as measured across ~1000 LC-MSruns of mass feature; Metabolite: identity of the validated metabolite.

These mass features served as the basis for the model that was appliedto predict the teratogenicity of chemical compounds in the blind studiesand treatments. This model was able to correctly predict theteratogenicity of seven of eight blinded drug treatments, with aspecificity of 100% and sensitivity of 80% and overall accuracy of 88%(Table 5).

TABLE 5 Results of the blind study where the teratogenicity wascorrectly predicted for 7 of 8 drugs using a random forest statisticalmodel. Blind # Drug Actual Predicted B1 Retinol Non Non B2 DoxylamineNon Non B3 Amiodarone Ter Ter B4 Rifampicin Ter Ter B5 Carbamazepine TerTer B6 Accutane Ter Non B7 Cyclophosphamide Ter Ter B8 Vitamin B1 NonNon

The random forest model was further refined by integrating outcomes fromblinded drugs into the model as known classifiers thereby increasing thenumber of non-teratogens and teratogens in the model, so that thetraining set consisted of 26 drug treatment experiments. Featureselection based on the variable importance measure mean decrease inaccuracy resulted in 18 features that were evaluated as a futurepredictive model. As a result, the overall accuracy of the model wasultimately increased to 92% (Table 6), i.e. the model was able tocorrectly predict 24 of the 26 drugs used in the training set. The modelwas clearly able to differentiate teratogens from non-teratogens intodistinct clusters when evaluated by multidimensional scaling (FIG. 3)which reflects clear differences in metabolomics endpoints betweentreatment classes.

TABLE 6 Model metrics for the 18 feature set prediction model AccuracySpecificity Sensitivity Teratogens Non-Teratogens Overall 100 87 87 10092

Following prediction of the blinds, a new model was created byincorporating the revealed blinds and more drugs into the training set(TS2, see Table 2). Evaluation of the receiver operating characteristic(ROC) curve of the model's performance demonstrated that the modelperforms in a robust manner (FIG. 4).

Thus, this model shows superior potential for future prediction of humandevelopmental toxicity in comparison to currently available assays, andthat using iterative modeling as more experiments are performed is apowerful benefit to the adoption of meaningful metabolic endpoints in ascreen. The predictive ability of this model is subject to continuousmonitoring in response to additional blinded drug treatments.

Statistically significant differences in the abundance of specificmetabolites were detected in drug-treated and control samples. One suchmolecule, asymmetric dimethylarginine (ADMA), exhibited a significantfold decrease in its abundance in response to valproic acid treatmentexhibiting similar changes for the strong teratogens: cytosinearabinoside, 5-fluorouracil, hydroxyurea, amiodarone andcyclophosphamide. ADMA is an inhibitor of nitric oxide synthase (NOS),an enzyme that converts L-arginine to L-citrulline which is necessaryfor neural tube closure (FIG. 5).

Valproate is known to cause neural tube defects (DiLiberti et al. 1984)while nitric oxide synthase activity is essential for neural tubeclosure (Nachmany et al. 2006). The novel alterations in the secretionof dimethylarginine, detected here, suggest that it can be anappropriate candidate biomarker for neural tube defects. Arginine levelswere also monitored in our data and usually showed opposite fold changesto those of dimethylarginine in response to several strong teratogens.To quantify the perturbation of arginine and ADMA in the hESCs as aresult of dosing, EICs (Extracted Ion Chromatograms) for these compoundswere constructed and integrated, then the ratio of the resulting areasfor controls vs. dosed were compared. These results indicate that theamount of perturbation may be directly related to the teratogenicity ofthe dosing compound. There are no false negatives resulting from thesemetrics, and only ascorbic acid and caffeine are false positives forteratogenicity (Table 7).

TABLE 7 Selected fold change ratios for arginine and dimethylarginine.EICs for these compounds were integrated, then the fold change of theresulting areas for controls vs. dosed were compared. Smaller foldchange ratios (between 0.9 and 1.1) show a good correlation withnon-teratogens, while greater changes (<0.9 and >1.1) correlate withteratogens. There are no false negatives for teratogenicity resultingfrom these metrics and only ascorbic acid and caffeine are falsepositives. Arg fold Stemina change/ADMA Arg/ADMA Classification Compoundfold change Prediction Non- Ascorbic Acid 1.28 Ter Teratogens Aspirin1.07 Non Caffeine 1.33 Ter Doxylamine (Blind 2) 0.97 Non Isoniazid 0.94Non Levothyroxine 1.03 Non Penicillin G 0.96 Non Folic Acid 1.08 NonRetinol (Blind 1) 1.03 Non Thiamine (Blind 8) 1.00 Non Teratogens5-Fluorouracil 43.93 Ter Methotrexate 2.54 Ter Accutane (Blind 6) 0.55Ter Amiodarone (Blind 3) 1.64 Ter Busulfan 1.12 Ter Carbamazepine (Blind5) 1.12 Ter Cyclophosphamide 1.56 Ter (Blind 7) Cytosine Arabinoside67.01 Ter Hydroxyurea 2.52 Ter Retinoic Acid 0.48 Ter Rifampicin (Blind4) 0.81 Ter Thalidomide 0.85 Ter Valproic Acid 2.11 Ter

Several metabolites that contributed to the random forest predictionmodel (PM) were further identified and subject to chemical identityvalidation by MS-MS. These include succinic acid, which showssignificant down regulation in its abundance in response to severalteratogens such as carbamazepine, cyclophosphamide, cytosinearabinoside, 5-fluorouracil, hydroxyurea, methotrexate, and valproicacid. Other small molecules that can contribute to the PM are:gamma-aminobutyric acid (GABA), isoleucine, aspartic acid, malic acid,glutamic acid, and histidine. These small molecules were significantlyaltered, according to the teratogenicity of the test compound and arecorrelated to each other on the basis of the biochemical pathways wherethey serve as intermediates. This is illustrated in FIG. 6.

For example, aspartic acid, dimethylarginine, and arginine arecomponents of the urea cycle. This cycle facilitates the removal ofdangerous ammonia through conversion of it to urea, which is excretedfrom the body. Succinic acid, isoleucine, and malate are part of thecitric acid cycle, which produces energy for cellular function. Bothnetworks are linked by glutamate and GABA, which in turn has a criticalrole in neuronal physiology.

Certain reactions in the urea cycle take place in the mitochondria,while the Kreb's cycle is active in the mitochondria in its entirety.Perturbations to the urea cycle can result in excess ammonia, which,among a vast array of pathological effects, has been correlated tonewborn deaths (Summar 2001). Interruption of citric acid cyclereactions compromises cellular energy metabolism with direct detrimentaleffects to cellular viability.

Increased concentrations of GABA were detected in the secretome of hESCsdosed with busulfan, among other teratogens. Dysfunctions in GABA,underlie well established neurological disorders such as epilepsy,language delay, and neurodevelopmental impairment, among others (Pearl &Bigson 2004). The neurodevelopmental toxicity of busulfan has beenpreviously reported in humans; specifically in utero exposure tobusulfan led to a spinal birth defect due to insufficient neural folddevelopment, although the mechanism was not defined (Abramovici et al.2005).

Example 7 Mechanistic Pathways of Developmental Toxicity

Altogether, metabolomics of hESCs detected statistically significantalterations to multiple small molecule metabolites which play a key rolein cellular physiology and human development. Several of these candidatebiomarkers were further validated by MS-MS mass spectrometry in order toconfirm their chemical identity. Significantly, despite the unsupervisednature of the analysis, many of these significant and validated smallmolecule metabolites participate in pathways that had been previouslysuggested to underlie developmental toxicity albeit not in cells derivedfrom human embryos. A list of validated small molecules and themetabolic networks they map to is provided in Table 8.

TABLE 8 Small Molecules and Metabolic Networks. METLIN KEGG HMDB CASPUBCHEM CHEBI KEGG Metabolic Network Name Formula Mass compound IDcompound ID compound ID compound ID Compound ID Compound ID Pathway IDor (Function) 2- C2H6O4S 125.9987 6987 C05123 HMDB03903 107-36-8 7866hsa00430 Taurine and Hydroxyethanesulfonate (isethionate) hypotaurine(sometimes misspelled as isothionate) metabolism Cysteic acid (cysteate)C3H7NO5S 169.0045 332 C00506 HMDB02757 13100-82-8 25701 17285 hsa00270Cysteine and methionine metabolism hsa00430 Taurine and hypotaurinemetabolism hsa04080 Neuroactive ligand- receptor interactionL-Cystathionine C7H14N2O4S 222.0674 39 C02291 HMDB00099 56-88-2 43925817482 hsa00260 Glycine, serine and threonine metabolism hsa00270Cysteine and methionine metabolism N1-Acetylspermidine C9H21N3O 187.16853323 C00612 HMDB01276 34450-16-3 496 17927 (cell growth anddifferentiation) Glycerophosphocholine C8H21NO6P 258.1106 370 C00670HMDB00086 28319-77-9 439285 16870 hsa00564 Glycerophospholipidmetabolism hsa00565 Ether lipid metabolism Spermine C10H26N4 202.2157255 C00750 HMDB01256 71-44-3 1103 15746 hsa00330 Arginine and prolinemetabolism hsa00410 beta-Alanine metabolism hsa00480 Glutathionemetabolism Spermidine C7H19N3 145.1579 254 C00315 HMDB01257 124-20-91102 16610 hsa00330 Arginine and proline metabolism hsa00410beta-Alanine metabolism hsa00480 Glutathione metabolism hsa02010 ABCtransporters 1-Methylnicotinamide C7H9N2O 137.0715 274 CO2918 HMDB006993106-60-3 457 16797 hsa00760 Nicotinate and nicotinamide metabolismNicotinamide C6H6N2O 122.048 1497 C00153 HMDB01406 98-92-0 936 17154hsa00760 Nicotinate and nicotinamide metabolism L-AcetylcarnitineC9H18NO4 204.1236 956 CO2571 HMDB00201 3040-38-8 18230 15960(facilitates movement of acetyl CoA into the matrices of mammalianmitochondria) Serotonin C10H12N2O 176.095 74 C00780 HMDB00259 50-67-95202 28790 hsa00380 Tryptophan metabolism hsa04080 Neuroactive ligand-receptor interaction hsa04540 Gap junction Melatonin C13H16N2O2 232.121273 C01598 HMDB01389 73-31-4 896 16796 hsa00380 Tryptophan metabolismhsa04080 Neuroactive ligand- receptor interaction GlutathioneC10H17N3O6S 307.0838 44 C00051 HMDB00125 70-18-8 124886 16856 hsa00270Cysteine and methionine metabolism hsa00480 Glutathione metabolismL-Malic acid C4H6O5 134.0215 118 C00149 HMDB00156 97-67-6 222656 30797hsa00020 Citrate cycle (TCA cycle) hsa00620 Pyruvate metabolism hsa00630Glyoxylate and dicarboxylate metabolism hsa05200 Pathways in cancerhsa05211 Renal cell carcinoma Maleic acid C4H4O4 116.011 4198 C01384HMDB00176 110-16-7 444266 18300 hsa00650 Butanoate metabolism hsa00760Nicotinate and nicotinamide metabolism Pyridoxine C8H11NO3 169.0739 2202C00314 HMDB00239 65-23-6 1054 16709 hsa00750 Vitamin B6 metabolismL-Histidine C6H9N3O2 155.0695 21 C00135 HMDB00177 71-00-1 6274 15971hsa00340 Histidine metabolism hsa00410 beta-Alanine metabolism hsa00970Aminoacyl-tRNA biosynthesis hsa02010 ABC transporters Succinic acidC4H6O4 118.0266 114 C00042 HMDB00254 110-15-6 1110 15741 hsa00020Citrate cycle (TCA cycle) hsa00190 Oxidative phosphorylation hsa00250Alanine, aspartate and glutamate metabolism hsa00350 Tyrosine metabolismhsa00360 Phenylalanine metabolism hsa00630 Glyoxylate and dicarboxylatemetabolism hsa00640 Propanoate metabolism hsa00650 Butanoate metabolismL-Arginine C6H14N4O2 174.1117 13 C00062 HMDB00517 74-79-3 6322 16467hsa00330 Arginine and proline metabolism hsa00472 D-Arginine and D-ornithine metabolism hsa00970 Aminoacyl-tRNA biosynthesis hsa02010 ABCtransporters hsa05014 Amyotrophic lateral sclerosis (ALS) AsymmetricDimethyl-L-arginine C8H18N4O2 202.143 6309 C03626 HMDB01539 102783-24-4123831 17929 (Inhibitor of Nitric Oxide Synthase in Arginine and prolinemetabolism) L-Cystine C6H12N2O4S2 240.0239 17 C00491 HMDB00192 hsa00270Cysteine and methionine metabolism hsa02010 ABC transportersL-Isoleucine C6H13NO2 131.0946 23 C00407 HMDB00172 73-32-5 791 17191hsa00280 Valine, leucine and isoleucine degradation hsa00290 Valine,leucine and isoleucine biosynthesis hsa00970 Aminoacyl-tRNA biosynthesishsa02010 ABC transporters Aspartic Acid C4H7NO4 133.0375 15 C00049HMDB00191 56-84-8 5960 17053 hsa00250 Alanine, aspartate and glutamatemetabolism hsa00260 Glycine, serine and threonine metabolism hsa00270Cysteine and methionine metabolism hsa00300 Lysine biosynthesis hsa00330Arginine and proline metabolism hsa00340 Histidine metabolism hsa00410beta-Alanine metabolism hsa00460 Cyanoamino acid metabolism hsa00760Nicotinate and nicotinamide metabolism hsa00770 Pantothenate and CoAbiosynthesis hsa00910 Nitrogen metabolism hsa00970 Aminoacyl-tRNAbiosynthesis hsa02010 ABC transporters hsa04080 Neuroactive ligand-receptor interaction Gamma-Aminobutyric acid (GABA) C4H9NO2 103.0633 279C00334 HMDB00112 56-12-2 119 16865 hsa00250 Alanine, aspartate andglutamate metabolism hsa00330 Arginine and proline metabolism hsa00410beta-Alanine metabolism hsa00650 Butanoate metabolism hsa04080Neuroactive ligand- receptor interaction Mevalonic acid C6H12O4 148.0736127 C00418 HMDB00227 150-97-0 439230 17710 hsa00900 Terpenoid backbonebiosynthesis 2′-deoxyuridine C9H12N2O5 228.0746 91 C00526 HMDB00012951-78-0 13712 16450 hsa00240 Pyrimidine metabolism

As discussed under Example 6, ADMA, an inhibitor of Nitric oxide (NO)metabolism, exhibited significant increases in fold changes in responseto exposure of hESCs to strong teratogens. NO has been identified as acandidate mechanism for neural tube disorders, and NO is essential fornormal axial development (Alexander et al. 2007). Monomethyl-L arginine,a specific inhibitor of NO, demonstrated NO is so critical for mammaliandevelopment, that both an excess as well as deficiency of NO can beembryotoxic (Lee & Juchau 2005). The present study is the first timethat two human intermediates in this network, arginine anddimethylarginine (FIG. 5, Table 7) were measured and exhibitedstatistically significant changes in response to several knowndisruptors of human development.

Other key small molecules changed as reported in the results section,share the same chemical network, namely GABA and glutamic acid. GABA isthe principal inhibitory neurotransmitter in the brain. Glutamatedysregulation has the potential to severely compromise neurogenesis,possibly contributing to cell death in specific regions of the brain(reviewed in (Bauman 1998)). Specifically, glutamate is vital forprogrammed cell death from development until three years of age. Notonly does the metabolite glutamate regulate neuronal survival or death,but it also plays a critical role in cognition, learning and memory(Tashiro et al. 2006). Glutamate and GABA are also known modulators ofneuronal migration during development (Lujan et al. 2005); henceconcomitant dysregulation of glutamate and GABA metabolism can providean important mechanism for human developmental toxicity.

Surprisingly, other small molecules reported herein, such as succinicacid, are likely to play synergistic roles with glutamic acid and GABAin the mechanism of teratogen-induced toxicity, given that simultaneouschanges to rate-limiting enzymes in both networks (GABA-transaminase andsuccinic semialdehyde dehydrogenase) are present in certainneuropsychiatric disorders, such as succinic semialdehyde dehydrogenasedeficiency or GABA aciduria (reviewed in (Pearl et al. 2007)). Althoughthis syndrome is inherited, in contrast to the environmental nature ofdevelopmental toxicity, it becomes even more striking that valproate hasbeen shown to aggravate symptoms in these patients, through furtherdetriment to GABA and succinic acid metabolism (Shinka et al. 2003),which is a direct indication of the potential of this hESC-baseddevelopmental toxicity screen to elucidate biologically meaningfulmechanisms of compound toxicity.

The metabolomics results presented here suggest that busulfan affectsGABA levels in the developing embryo, which in turn can underlie neuraldevelopmental disruption. These examples illustrate how metabolomicsunravels mechanistic networks of developmental toxicity through directanalysis of secreted or excreted metabolites from hESCs dosed with knownteratogens. In doing so, it is quite possible to model the potential fordevelopmental toxicity of new drugs screened in preclinical developmentwith a high degree of predictability while providing information aboutthe mechanisms of toxicity. Further studies will allow classification ofcompounds into subgroups of developmental toxicity such neuraldevelopmental disruptors or those likely to cause structuralmalformations.

In one embodiment five or more of the validated small molecules listedin Table 8 are used to predict the teratogenicity of a test compoundaccording to the methods of the present invention. In other embodiments,ten or more of the validated small molecules listed in Table 8 are usedto predict the teratogenicity of a test compound according to themethods of the present invention.

Example 8 Metabolic Networks Involved in Developmental Toxicity

Two experimental systems were deployed per chemical: viability studiesand metabolomics studies. These assays were performed in two phases.Cell viability assays were performed to establish the threeconcentrations to dose hES cells for metabolomic studies. First, hEScells were dosed with eight concentrations of each then cell viabilitymeasurements were made using the MultiTox-Fluor cell based assay(Promega). Concentration curves for each chemical were calculated todetermine the three concentrations for the metabolomics analysis. Thefinal concentrations employed in this study were those that caused nocell death and minimal cell death, if possible.

For metabolomic analysis, hES cells were dosed at the threeconcentrations for each chemical compound based on the cell viabilitydata. Media controls (no cells), dosed media controls (no cells withdosed media), and controls (cells with undosed media) were also includedin the experimental design (FIG. 1). Spent media was collected followinga three day dosing period. The collected media was immediately quenchedin acetonitrile then stored at −80° C. until later analysis.

In both the viability and metabolomics steps, 96-well plates were seededwith 250,000 cells/well of WA09 hES cells. These cells were “dosed” forthree days. Each day for three days, the spent media was removed andreplaced with mTeSR®1 media containing the designated compound. Eachcompound stock solution was made in DMSO and each final solution used todose hES cells contained 0.1% DMSO. Spent media samples were collectedon the fourth day and prepared for metabolomic analysis.

Sample Preparation:

In order to isolate small molecular weight compounds (<10 kDa) fromsamples for metabolomics experimentation, the Millipore MultiscreenUltracel-10 molecular weight cut off plates were used. These plates werefirst washed with a 0.1% sodium hydroxide solution and then twice withwater to remove contaminant polymer product. The quenched sample wereadded to the washed filter which was centrifuged at 2000×g forapproximately 240 minutes at 4° C., the flowthrough was collected, thendried overnight in a SpeedVac. The dried samples were reconstituted in70 μL, of 1:1 0.1% formic acid in water:0.1% formic acid in acetonitrileand transferred to a 96-well plate.

LC-MS Experiment:

Samples were analyzed in both ESI positive and ESI negative modes on anAgilent QTOF instrument, operated in high resolution, extended dynamicrange mode. Two Phenomenex Luna HILIC columns; 100×3 mm; P/N00D-4449-Y0, S/N 440333-5, and S/N 512570-3 were used for the analysis.

Data Processing:

Sample Naming Scheme

Sample names used for statistical analysis are coded with theexperimental compound name (ST003G.74.A, ST003G.75.B, etc.), the doselevel (High (H), Medium (M), or Low (L)), and repetitions (a-h). Thesample name “ST003G.74.A _H_b” can be decoded as experimental compound74A, dose level “high,” repetition b and the sample name“ST003G.84.K_L_b” can be decoded as experimental compound 84K, doselevel “low,” repetition b and so on.

Data Processing

mzData File Creation

Agilent raw data files were converted to the open source mzData fileformat using Agilent MassHunter Qual software version 3.0. During theconversion process, deisotoping (+1 charge state only) was performed onthe centroid data and peaks with an absolute height less than 400(approximately double the typical average instrument background level).The resulting mzData files contain centroid data of deisotoped (+1charge state only) peaks that have an absolute height greater than 400counts.

Mass Feature Creation and Integration.

Peak picking and feature creation were performed using the open sourcesoftware library XCMS. Mass features (peaks) were detected using thecentwave algorithm. Following peak picking deviations in retention timeswere corrected using the obiwarp algorithm that is based on a non-linearclustering approach to align LC-MS samples. Mass feature bins or groupswere generated using a density based grouping algorithm. After the datahad been grouped into mass features, missing features were integratedbased on retention time and mass range of a feature bin using theiterative peak filling. Feature intensity is based on the Mexican hatintegration values of the feature extracted ion chromatograms.

Solvent/Extraction Blank Filter

The extraction blank filter removes ions associated with the sampleextraction process and background ions present in the LC-MS system.Features were removed from the metabolomics dataset if the average inthe experimental samples was less than five times the average abundancein the extraction blanks.

Contamination DB Filter

The contamination DB filter removes features with a mass match within 20ppm to entries in Stemina's proprietary database which contains a numberof contaminants such as plasticizers and PEG compounds identified inprevious studies. Features are removed without respect to retention timeif they match a contaminant or a common charge specific adduct of acontaminant.

PCA Based Outlier Removal

Sample outlier detection and removal is performed on the log based 2transformed pareto scaled abundance values by experimental factor useNIPALS based PCA. A distance measurement is used to flag and removeoutlier LC-MS samples that are outside the 0.975 quantile of thedistance measurements.

Abundance and Reproducibility Filter

Prior to statistical analysis, features were filtered by factor (e.g.experimental compound by dose) to remove features that did not exhibitabundance greater than 12,500 (ESI negative mode) or 50,000 (ESIpositive mode) in 66% of the LC-MS runs for at least one dose level (L,M, H) of at least one experimental compound (e.g., ST003G.82.I). Thisfilter selects against spurious low abundance features at the level ofdetection that are not reproducibly measured, and features that may nothave peak shapes amenable to reproducible detection and/or integration.This filter typically removes a large portion of the metabolomicsdataset, and focuses the analysis on the most reliable and valuablefeatures. For example a feature with abundance values greater than12,500 in 70% of the negative mode LC-MS samples in one dose level ofone experimental compound and abundance values greater than 12,500 innone of the other experimental compound by dose combinations would passthe filter because at least one experimental compound by dose factorsatisfies the filter criteria.

Data Transformation and Normalization.

All data were log base two transformed. Normalization for each factorlevel was performed by subtracting the column (sample) mean and dividingby the row (feature) standard deviation for each value (autoscaling).

Differential Analysis of Mass Features (Univariate)

Mass features were evaluated under the null hypothesis that nodifference is present between the means of experimental classes and thealternative hypothesis that there is a difference between experimentalclasses. Welch two sample T-tests were performed as a parametric methodthat does not assume equal variances of the experimental classes. Aone-way ANOVA was performed on each experimental compound to evaluatethe difference in means across the three dose levels. Tukeys post hoctests were performed to identify significant differences between thedose levels. Following statistical analysis false discovery rates werecontrolled for multiple testing using the Benjamini-Hochberg (1995)method of p value correction of the ANOVA and Welch T-tests.

Analysis of Mass Features (Multivariate)

Annotation of mass features was carried out by comparing the m/z massvalues of the mass features to Stemina's internal metabolite databasecontaining records from multiple public databases such as HMDB, KEGG,PubChem Compound, and METLIN and company-specific metabolite data. Thefeatures were annotated with respect to the appropriate adducts for eachESI mode. The identities of all mass features were not validated andtherefore all annotations are putative.

Identification of Mass Features

Annotation of mass features was carried out by comparing the m/z massvalues of the mass features to Stemina's internal metabolite databasecontaining records from multiple public databases such as HMDB, KEGG,PubChem Compound, and METLIN and company-specific metabolite data. Thefeatures were annotated with respect to the appropriate adducts for eachESI mode. The identities of all mass features were not validated andtherefore all annotations are putative.

Networks Analysis

Pathways enrichment analysis was performed by mapping annotated massfeatures for each experimental compound to human metabolic networksusing KEGG compound ids. Hypergeometric p-values and false discoveryrates (FDR) were used to assign a quantitative measure of statisticalsignificance to each network. Features derived from ESI negative andpositive mode for each experimental compound were pooled for thisanalysis. False positive results can be generated by isobaric compoundsthat generate multiple “hits” in a pathway from the same mass, so uniquemasses instead of unique compound ids were used for these calculations.The relevant parameters used to calculate hypergeometric p-values foreach pathway were: the number of unique mass “hits”, the number ofunique masses in the network, and the total number of unique masses inall of the human networks in the KEGG database. For each experimentalcompound, the p-values for the derived networks were converted to FDRusing the Benjamini and Hochberg (1995) correction.

Selection of Interesting Features

Feature Selection was performed on a per compound basis using a one-wayanova evaluating the difference of dose level means and on a per dosebasis using Welch T-tests and PLS-DA VIP score. Features were selectedfor further evaluation if they had a Welch FDR<0.05 or a PLS-DA VIPscore>20 with at least a 50% fold change and control cells showed atleast a 40% difference to control media (secreted, consumed, oridentified), or Anova FDR<0.05 and a difference between 0.1× and 10×dose was at least 50%. If a feature was selected as interesting in adrug or dose level comparison it was then evaluated experiment wide forfold changes. Following feature selection only significant featuresputatively annotated as mammalian in origin and present on KEGG networkdiagrams were further evaluated. Pathway enrich analysis was thenperformed on the selected features and features in networks exhibiting astatistically significant enrichment were further evaluated for foldchanges. These selection criteria focused the analysis on biochemichalpathways.

Results and Discussion:

Metabolomic analysis of the cell culture supernatant extracts resultedin a set of 324 features in ESI positive mode and 307 features in ESInegative mode after selection for statistical significance and putativemammalian annotations. Following selection, features were passed througha quality control evaluation of extracted ion chromatograms (EICs) toconfirm the validity of individual mass features. Features passingquality control were further evaluated to confirm estimated foldchanges. After removing poor quality and duplicate features, theremaining ESI positive and ESI negative mode features were combined intoa unified dataset for evaluation of pathway enrichment by treatment.These mass features mapped to 86 different KEGG networks of which 15exhibited a statistically significant (FDR<0.1) enrichment of annotatedfeatures in at least one treatment (Table 9). EICs for all metabolitesin 4 networks that exhibited the most significant enrichment wereplotted and feature quality and fold changes were evaluated.

Changes in metabolites associated with the urea cycle, glutamatemetabolism, and the citric acid cycle have been associated with exposureof hES cells to teratogens. Several of the annotated mass features wereevaluated for changes in at least two dose levels (unless otherwisenoted) of the blinded compounds. Succinic acid (TCA cycle) is generallydecreased in hES cells treated with teratogens and unchanged innon-teratogens. In this study, succinic acid was decreased in at leasttwo dose levels in cells treated with ST003G.74.A, ST003G.75.B,ST003G.76.C, ST003G.77.D, ST003G.80.G, ST003G.81.H. Treatment withteratogens leads to a decrease in accumulation of dimethylarginine (DMA,urea cycle) usually observed in combination with increases in arginine(arginine and proline metabolism) secreted by hES cells. In the currentstudy, blinded compounds exhibited increased secretion of DMA inST003G.82.I, ST003G.83.J, ST003G.84.K and ST003G.85.L, a mixed responsein ST003G.77.D and ST003G.78.E, and decreased accumulation inST003G.80.G while arginine was not significantly changed in this study.Glutamic Acid (glutamate metabolism) exhibited increased secretion inST003G.74.A and ST003G.84.K, a mixed response in ST003G.78 E andST003G.80.G following treatment while hES cells following treatment withteratogens show a pattern of either increased or decreased levels ofglutamic acid. γ-Aminobutyric acid (GABA, neuroactive ligand-receptor)which can be increased in hES after treatment with teratogens wasincreased in ST003G.84.K and decreased in ST003G.75.B. Aspartic acid(urea cycle, glutamate metabolism) is generally increased in the mediaof hES cells following treatment with teratogens was decreased inST003G.74.A and ST003G.75.B and increased in ST003G.77.D andST003G.80.G. Malic acid, which is generally changed in teratogens in amore extreme manner than non-teratogens exhibited extreme fold changesin the high dose levels of ST003G.78.E, ST003G.79.F, ST003G.80.G,ST003G. 82.I, and ST003G.85.L.

TABLE 9 Summary of pathway enrichment analysis performed on positive andnegative features. The values indicate the number of unique KEGG IDannotations identified across dose levels for each drug. Cells havingunderlined values indicate a statistically significant enrichment (FDR <0.1) in at least one treatment dose level. Pathway Description 74.A 75.B76.C 77.D 78.E 79.F 80.G 81.H 82.I 83.J 84.K 85.L Alanine, aspartate 0 00 0 1 1 4 0 7 0 0 1 and glutamate metabolism Arginine and proline 1 0 23 2 0 6 0 15 0 1 1 metabolism Ascorbate and 0 0 1 4 1 0 2 0 11 5 0 2aldarate metabolism Citrate cycle (TCA 0 0 0 1 2 0 2 0  5 0 0 1 cycle)Cysteine and 0 0 0 0 0 0 4 0  8 0 0 2 methionine metabolism Galactose 00 0 6 0 0 0 0 13 9 0 2 metabolism Glutathione 0 0 1 3 1 1 4 0  3 0 0 1metabolism Glyoxylate and 0 0 0 1 3 0 2 1  6 0 0 3 dicarboxylatemetabolism Nicotinate and 0 0 1 0 5 0 1 0  6 0 0 5 nicotinamidemetabolism Pantothenate and 0 0 1 1 3 0 1 0  5 0 0 3 CoA biosynthesisPentose and 0 0 0 0 1 1 1 0 13 1 0 3 glucuronate interconversionsPentose phosphate 0 0 0 3 0 1 1 0  6 4 0 1 pathway Propanoate 0 0 1 0 10 7 0  5 0 0 3 metabolism Pyruvate metabolism 0 0 0 1 1 0 1 0  8 0 0 6Vitamin B6 0 0 0 0 0 1 2 0  6 1 0 3 metabolism

Example 9 Prediction of Teratogenicity of Test Compounds

The potential teratogenicity of the individual compounds analyzed inExample 8 were further validated.

Data Analysis and Results:

Prediction of teratogenicity was performed using a partial least squaresdiscriminate analysis (PLS-DA) model based on metabolic changes observedin the spent cell culture media (secretome) from WA09 human embryonicstem (hES) cells treated with pharmaceutical agents. The PLS-DAclassifier model was trained on data previously acquired in the DevToxproject for the secretome of hES cells that had been treated withtherapeutic circulating doses of 22 pharmaceutical agents of knownteratogenicity (Table 11). These included 11 known teratogens and 11known non-teratogens. The current model is based on the mean fold change(treatment versus its associated intra-experimental control) of 15metabolites common among the secretome of hES cells treated withpharmaceutical agents and unknown chemical compounds. The results ofthis model for the DevTox drugs are shown in Table 11. For this study ofEPA compounds, the experiment represents the first instance of thisPLS-DA model as applied to the prediction of non-pharmaceuticalenvironmental toxicants.

TABLE 10 Features utilized in the PLS-DA prediction of Teratogenicity.Metabolites in bold font indicate a previously validated metabolite.Annotation m/z RT Polarity methylsulfonylacetonitrile 120.0116 618ESI(+) Aspartic Acid 134.0460 431 ESI(+) N*-Acetylspermidine 188.1760431 ESI(+) Dimethyl-L-arginine 203.1504 445 ESI(+) Unknown 215.1387 466ESI(+) L-Cystathionine 223.0750 593 ESI(+) Unknown 234.8904 246 ESI(+)Unknown 251.0666 105 ESI(+) Unknown 403.0839 653 ESI(+) GABA 102.0561467 ESI(−) Fumaric acid 115.0057 111 ESI(−) Valine 116.0712 309 ESI(−)Succinic acid 117.0190 82 ESI(−) Aspartic acid 132.0299 472 ESI(−)Pantoic acid 147.0658 81 ESI(−)

TABLE 11 Prediction of teratogencity by PLS-DA-DevTox pharmaceuticalcompounds that were utilized in the PLS-DA Model and their resultingpredictions. The high (H = 10x) and low (L = 0.1x) dose treatments ofthe pharmaceutical agents utilized in the training set are included as areference (Note: M = 1x, corresponds to the circulating dose. This dosewas used in the training of the PLS-DA model and hence omitted fromprediction table). Bold font indicates non-teratogen at circulatingdose, regular font indicates teratogen at circulating dose. DrugTreatment Prediction % Non % Ter Confidence 5-Fluorouracil_H Ter 0.320.68 0.36 5-Fluorouracil_L Ter 0.28 0.72 0.44 Accutane_H Ter 0.3 0.7 0.4Accutane_L Ter 0.33 0.67 0.34 Busulfan_H Ter 0.28 0.72 0.44 Busulfan_LTer 0.29 0.71 0.42 Carbamazepine_H Ter 0.37 0.63 0.26 Carbamazepine_LNon 0.5 0.5 0 Cyclophosphamide_H Ter 0.45 0.55 0.1 Cyclophosphamide_LTer 0.41 0.59 0.18 CytosineArabinoside_H Ter 0.36 0.64 0.28CytosineArabinoside_L Ter 0.33 0.67 0.34 Hydroxyurea_H Ter 0.32 0.680.36 Hydroxyurea_L Non 0.64 0.36 0.28 Methotrexate_H Ter 0.42 0.58 0.16Methotrexate_L Ter 0.48 0.52 0.04 RetinoicAcid_H Ter 0.3 0.7 0.4RetinoicAcid_L Ter 0.3 0.7 0.4 Rifampicin_H Ter 0.27 0.73 0.46Rifampicin_L Ter 0.46 0.54 0.08 Thalidomide_H Ter 0.3 0.7 0.4Thalidomide_L Non 0.65 0.35 0.3 VPA_H Ter 0.34 0.66 0.32 VPA_L Ter 0.430.57 0.14 Ascorbic Acid_H Non 0.57 0.43 0.14 Ascorbic Acid_L Non 0.570.43 0.14 Caffeine_H Non 0.53 0.47 0.06 Caffeine_L Non 0.58 0.42 0.16Diphenhydramine_H Non 0.73 0.27 0.46 Diphenhydramine_L Non 0.76 0.240.52 Doxylamine_H Ter 0.38 0.62 0.24 Doxylamine_L Non 0.58 0.42 0.16Folic Acid_H Non 0.59 0.41 0.18 Folic Acid_L Non 0.59 0.41 0.18Isoniazid_H Non 0.59 0.41 0.18 Isoniazid_L Non 0.76 0.24 0.52Levothyroxine_H Non 0.59 0.41 0.18 Levothyroxine_L Non 0.69 0.31 0.38PenicillinG_H Non 0.57 0.43 0.14 PenicillinG_L Non 0.55 0.45 0.1Retinol_H Non 0.65 0.35 0.3 Retinol_L Non 0.75 0.25 0.5 Saccharin_H Non0.8 0.2 0.6 Saccharin_L Non 0.75 0.25 0.5 Thiamine_H Non 0.78 0.22 0.56Thiamine_L Non 0.82 0.18 0.64

TABLE 12 Prediction of teratogencity by PLS-DA for EPA compounds. % Nonand % Ter are the PLS-DA generated class probabilities. Confidence isthe difference between class probabilities. Confidence values less than0.1 are considered inconclusive with respect to the class prediction.Treatment Prediction % Non % Ter Confidence ST003G.74.A_H Ter 0.35 0.650.3 ST003G.74.A_M Ter 0.44 0.56 0.12 ST003G.74.A_L Non 0.59 0.41 0.18ST003G.75.B_H Ter 0.49 0.51 0.02 ST003G.75.B_M Non 0.65 0.35 0.3ST003G.75.B_L Ter 0.4 0.6 0.2 ST003G.76.C_H Ter 0.42 0.58 0.16ST003G.76.C_M Non 0.64 0.36 0.28 ST003G.76.C_L Non 0.75 0.25 0.5ST003G.77.D_H Ter 0.39 0.61 0.22 ST003G.77.D_M Ter 0.38 0.62 0.24ST003G.77.D_L Ter 0.37 0.63 0.26 ST003G.78.E_H Non 0.69 0.31 0.38ST003G.78.E_M Non 0.64 0.36 0.28 ST003G.78.E_L Non 0.59 0.41 0.18ST003G.79.F_H Ter 0.37 0.63 0.26 ST003G.79.F_M Non 0.51 0.49 0.02ST003G.79.F_L Ter 0.45 0.55 0.1 ST003G.80.G_H Non 0.59 0.41 0.18ST003G.80.G_M Non 0.63 0.37 0.26 ST003G.80.G_L Non 0.66 0.34 0.32ST003G.81.H_H Non 0.67 0.33 0.34 ST003G.81.H_M Non 0.69 0.31 0.38ST003G.81.H_L Non 0.75 0.25 0.5 ST003G.82.I_H Ter 0.3 0.7 0.4ST003G.82.I_M Ter 0.42 0.58 0.16 ST003G.82.I_L Non 0.57 0.43 0.14ST003G.83.J_H Non 0.73 0.27 0.46 ST003G.83.J_M Non 0.75 0.25 0.5ST003G.83.J_L Non 0.75 0.25 0.5 ST003G.84.K_H Non 0.73 0.27 0.46ST003G.84.K_M Non 0.79 0.21 0.58 ST003G.84.K_L Non 0.81 0.19 0.62ST003G.85.L_H Ter 0.45 0.55 0.1 ST003G.85.L_M Ter 0.44 0.56 0.12ST003G.85.L_L Ter 0.41 0.59 0.18Conclusions:

The prediction model that has been developed classifies the EPA-providedchemical agents ST003G.74.A, ST003G.75.B, ST003G.77.D, ST003G.82.I,ST003G.85.L as potential teratogens, and the chemical agentsST003G.76.C, ST003G.78.E, ST003G.80.G, ST003G.81.H, ST003G.83.J,ST003G.84.K as potential non-teratogens. The chemical agent ST003G.76.0is predicted as a teratogen only at the highest dose level. See Table12.

Doxylamine was added to the test set as a reference pharmaceuticaltreatment (ST003G-85-L). Doxylamine has been ranked by the FDA as apregnancy category B drug, which means that animal studies show no riskof that particular drug inducing birth defects and there are no studiesin pregnant women. This compound was analyzed in the developmentaltoxicity assay. At the low and medium dose, Doxylamine was classified asa non-teratogen, while at the high concentration; it was classified as ateratogen (Table 11). In these studies all three concentrations (low,medium, and high) of Doxylamine was classified as being a teratogen. Theconcentrations of Doxylamine used in these studies and the correspondingteratogenicities assigned at each concentration are shown in the tablebelow. There appears to be a critical concentration which causes theclassification of Doxylamine to switch from a non-teratogen to ateratogen and, according to our data, it is between 0.38 and 1 μM.

TABLE 13 Doxylamine dose levels and PLS-DA teratogenocity predictions.[Doxylamine] Teratogenicity (μm) Project Classification 0.038 devTox lowNon 0.38 devTox medium Non 1 EPA low Ter 3.8 devTox high Ter 10 EPAmedium Ter 100 EPA high TerTo ensure the teratogenicity classifications are not merely a reflectionof cell viability, the cell viability data was analyzed (FIG. 23). Asindicated below, there is no correlation between teratogenicityclassification and cell viability, and at 1 μM Doxylamine the cells areactually thriving (FIGS. 23, a to c). There is some cell death at 0.38μM, however, at this concentration, Doxylamine was still not classifiedas a teratogen. This example of the prediction on the teratogenicity ofDoxylamine helps substantiate the present model of teratogenicity.

Example 10 Pathway Interpretation

Several biochemical pathways with a statistically significant enrichmentof annotated mass features were further evaluated. Of most interest inthe present findings are nicotinate and nicotinamide metabolism,pantothenate and CoA biosynthesis, glutathione metabolism, and arginineand proline metabolic networks. These pathways were examined toelucidate connections between these pathways and birth defects.Metabolites within the pathways which are marked with a black circle arethose with unique masses while those which are marked with a grey circleare isobaric and may be another metabolite with the same molecularweight.

Nicotinate and Nicotinamide Metabolic Network:

Nicotinate and nicotinamide are precursors of the coenzymesnicotinamide-adenine dinucleotide (NAD+) and nicotinamide-adeninedinucleotide phosphate (NADP+), which, when reduced, are importantcofactors in many redox reactions. When nicotinic acid is deficient,pellagra can result. It was found that mutations in the nicotinamideN-methyl transferase (NNMT) could lead to risk of spina bifida (Lu etal., Mol. Teratology, 82:670-675, 2008) and it is possible thatalterations to this pathway could lead to birth defects and thus,measurements of fold change of metabolites in this pathway couldindicate a compound's teratogenicity

Pantothenate and CoA Biosynthesis Network:

A significant number of putative metabolite annotations from thepantothenate and CoA biosynthesis network exhibited statisticallysignificant changes across a number of compounds. The network figure forthe Pantothenate and CoA biosynthesis network shows the putativeannotations, marked with either a black circles, or a grey circle (thosemetabolites highlighted that are grey circles are isobaric while thosethat are black circles have unique masses.)

The pantothenate and CoA biosynthesis network produces CoA whichattaches to a long-chain fatty acid to eventually form acetyl-CoA whichenters the TCA cycle resulting in ATP synthesis. Thus aberrations tothis network can result in energy production abnormalities, which can,in turn, cause severe impairment of cellular processes. Of mostimportance in the network is the pantothenate availability, as thephosphorylation of this metabolite is the rate-limiting step of CoAproduction and it has been observed that impaired energy result alongwith neurological symptoms (Rock et al., J. Biol. Chem., 275:1377-1383,2000) as a result of low levels of pantothenate. Furthermore, it wasfound that maternal pantothenate deficiency results in a teratogeniceffect on the fetus (Nelson et al., J. Nutr., 62:395-405, 1957; Baker etal., Am. J. Clin. Nutr., 28:56-65, 1975). Given these associations ofalterations to the pantothenate network and birth defects, it isplausible to correlate chemicals which cause abundance changes ofmetabolites within the pantothenate network with the likelihood thatparticular chemicals causing these changes may in turn have the abilityto disrupt human development, and possibly induce birth defects.

Glutathione Network:

The glutathionine network plays a role in oxidative stress. Glutathione,an essential metabolite of the network, can exist in a reduced oroxidized state. In its reduced state, glutathione has the ability toprotonate free radicals and, thus, acts as an antioxidant. Oxidativestress is associated with neurodegenerative disease (Simonian et al.,Ann Rev Pharm. Tox., 36:83-106, 1996), pulmonary disease (Repine et al.,Am. J. Resp. Critical Care Med., 156:341-357, 1997), and has even beenrelated to preeclampsia (Walsh et al., Semin. Reprod. Med., 16:93-104,1998). There have been several studies which relate glutathione levelswith birth defects. For example, Isibashi et al. had found thatglutathione depletion and oxidative stress strongly implicate birthdefects in animals (Isibashi et al., Free Rad. Biol. Med., 22:447-454,1997). Zhao et al. also found such a relationship in humans anddiscovered that women with neural tube defect pregnancies had higherlevels of oxidized glutathione than the control group (Zhao et al.,Birth Defects Research Part A: Clinical and Molecular Teratology,76:230-236, 2006). Due to this association of the glutathione networkand birth defects, it is possible to further study the fold changes forthe metabolites within this network in order to classify each chemicalcompound as a potential teratogen or not.

Arginine and Proline Metabolic Network:

Several statistically significantly altered small molecules within thearginine and proline metabolic network were found. Most interesting isthe presence of dimethylarginine, arginine, and citrulline. Nitric oxidesynthase converts L-Arginine to L-Citrulline. Dimethylarginine is aninhibitor of Nitric Oxide Synthase. Studies have found that nitric oxidesynthase is essential for neural tube closure (Nachmany et al., J.Neurochem., 96:247-253, 2006) and so modifications to this reaction andto levels of L-citrulline and L-arginine could indicate a chemicalcompound's ability to induce birth defects.

All references cited herein are incorporated by reference. In addition,the invention is not intended to be limited to the disclosed embodimentsof the invention. It should be understood that the foregoing disclosureemphasizes certain specific embodiments of the invention and that allmodifications or alternatives equivalent thereto are within the spiritand scope of the invention as set forth in the appended claims.

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What is claimed is:
 1. A method of predicting human developmentaltoxicity of a test compound, the method comprising the steps of: (a)culturing human stem-cell like cells (hSLCs): (i) in the presence of afirst known developmental toxicant; and (ii) in the absence of the firstknown developmental toxicant; (b) detecting a plurality of metaboliteshaving a molecular weight of less than about 3000 Daltons associatedwith hSLCs exposed to the first known developmental toxicant incomparison with hSLCs not exposed to the first known developmentaltoxicant in order to identify a difference in metabolic response ofhSLCs exposed to the first known developmental toxicant in comparisonwith hSLCs not exposed to the first known developmental toxicant;wherein the plurality of metabolites is detected by mass spectrometry;(c) generating a set of mass features associated with exposure of hSLCsto the first developmental toxicant by analyzing the difference inmetabolic response; (d) repeating steps a)-c) multiple times, each timewith a different known developmental toxicant; (e) grouping massfeatures generated from each exposure to a developmental toxicant toobtain a reference profile of mass features correlating withdevelopmental toxicity; (f) culturing hSLCs; (i) in the presence of atest compound; and (ii) in the absence of the test compound; (g)detecting a plurality of metabolites having a molecular weight of lessthan about 3000 Daltons associated with hSLCs exposed to the testcompound in comparison with hSLCs not exposed to the test compound inorder to identify a difference in metabolic response of hSLCs exposed tothe test compound in comparison with hSLCs not exposed to the testcompound; wherein the plurality of metabolites is detected by massspectrometry; and (h) generating a set of mass features associated withexposure of hSLCs to the test compound by analyzing the difference inmetabolic response; (i) comparing the set of mass features associatedwith exposure of hSLCs to the test compound of step (h) to the referenceprofile of mass features correlating with developmental toxicity of step(e) to predict the human developmental toxicity of the test compound. 2.The method of claim 1, wherein the hSLCs cultured in the absence of aknown developmental toxicant are further cultured during step a) and/orstep (f) in the presence of a known non-developmental toxicant.
 3. Themethod according to claim 1, wherein the hSLCs comprise human embryonicstem cells (hESCs), human induced pluripotent (iPS) cells, or humanembryoid bodies.
 4. The method according to claim 1, wherein thereference profile comprises a biomarker profile characteristic of hSLCresponse to a developmental toxicant.
 5. The method according to claim1, wherein the mass spectrometry is liquid chromatography/electrosprayionization mass spectrometry.
 6. The method according to claim 1,wherein the reference profile of mass features correlating withdevelopmental toxicity comprises five or more of the small moleculeslisted in Table
 8. 7. The method according to claim 1, wherein thereference profile of mass features correlating with developmentaltoxicity comprises ten or more of the small molecules listed in Table 8.8. The method according to claim 1, wherein the reference profile ofmass features correlating with developmental toxicity comprises one ormore of the small molecules listed in Table
 10. 9. The method accordingto claim 1, wherein the reference profile of mass features correlatingwith developmental toxicity comprises the fifteen small molecules listedin Table
 10. 10. The method according to claim 1, wherein massspectrometry comprises gas phase ion spectrophotometer and/orlaser-desorption/ionization mass spectrometry.
 11. The method accordingto claim 1, wherein mass spectrometry comprises matrix assisted laserdesorption/ionization (MALDI) mass spectrometry and/or surface-enhancedlaser desorption/ionization (SELDI).
 12. The method according to claim1, wherein mass spectrometry comprises MALDI/TOF (time-of-flight),SELDI/TOF, liquid chromatography-mass spectrometry (LC-MS), gaschromatography-mass spectrometry (GC-MS), high performance liquidchromatography-mass spectrometry (HPLC-MS), capillaryelectrophoresis-mass spectrometry, nuclear magnetic resonancespectrometry, tandem mass spectrometry, secondary ion mass spectrometry(SIMS), and/or ion mobility spectrometry.
 13. The method according toclaim 1, wherein culturing the hSLCs in the presence of a knowndevelopmental toxicant of step (a) comprises culturing hSLCs at a doseof the known developmental toxicant selected from: a dose correspondingto the EC50 or IC50 concentration of the known developmental toxicant; adose below the EC50 or IC50 concentration of the known developmentaltoxicant; or a dose of the known developmental toxicant equivalent tothe circulating concentration in maternal serum.
 14. The methodaccording to claim 1, wherein culturing the hSLCs in the presence oftest compound comprises culturing hSLCs at a dose of the test compoundselected from: a dose representing the EC50 or IC50 concentration of thetest compound; a dose below the EC50 or IC50 concentration of the testcompound; or a dose of the test compound equivalent to the circulatingconcentration in maternal serum.
 15. The method according to claim 1,wherein the known developmental toxicant is selected fromdiphenylhydantoin, methotrexate, 5-fluorouracil, accutane, amiodarone,busulfan, carbamazepine, cyclophosphamide, cytosine arabinoside,hydroxyurea, retinoic acid, rifampicin, thalidomide, and/or valproicacid.
 16. The method according to claim 1, wherein step (a) is repeatedfor each of the known developmental toxicants 5-fluorouracil, busulfan,cytosine arabinoside, hydroxyurea, retinoic acid, thalidomide, andvalproic acid.
 17. The method according to claim 16, wherein thereference profile of mass features correlating with developmentaltoxicity comprises the 142 mass features shown in Table
 4. 18. Themethod according to claim 16, wherein step (a) is further repeated foreach of the known developmental toxicants diphenylhydantoin,methotrexate, accutane, amiodarone, carbamazepine, cyclophosphamide, andrifampicin.
 19. The method according to claim 2, wherein step (a) isrepeated for each of the known developmental toxicants and knownnon-developmental toxicants shown in Table
 11. 20. The method accordingto claim 19, wherein the reference profile of mass features correlatingwith developmental toxicity comprises the 15 mass features shown inTable
 10. 21. A method of producing a reference profile of mass featurescorrelating with developmental toxicity, the method comprising the stepsof: (a) culturing human stem-cell like cells (hSLCs): (i) in thepresence of a first known developmental toxicant; and (ii) in theabsence of the first known developmental toxicant; (b) detecting aplurality of metabolites having a molecular weight of less than about3000 Daltons associated with hSLCs exposed to the first knowndevelopmental toxicant in comparison with hSLCs not exposed to the firstknown developmental toxicant in order to identify a difference inmetabolic response of hSLCs exposed to the first known developmentaltoxicant in comparison with hSLCs not exposed to the first knowndevelopmental toxicant; wherein the plurality of metabolites is detectedby mass spectrometry; (c) generating a set of mass features associatedwith exposure of hSLCs to the first developmental toxicant by analyzingthe difference in metabolic response; (d) repeating steps a)-c) multipletimes, each time with a different known developmental toxicant; (e)grouping mass features generated from each exposure to a developmentaltoxicant to obtain a reference profile of mass features correlating withdevelopmental toxicity.
 22. The method of claim 21, wherein the hSLCscultured in the absence of a known developmental toxicant are furthercultured during step a) in the presence of a known non-developmentaltoxicant.